{"id":9,"date":"2026-04-12T12:31:05","date_gmt":"2026-04-12T12:31:05","guid":{"rendered":"https:\/\/www.devopsschool.com\/tutorials\/alibaba-cloud-platform-for-ai-pai-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-machine-learning\/"},"modified":"2026-04-12T12:31:05","modified_gmt":"2026-04-12T12:31:05","slug":"alibaba-cloud-platform-for-ai-pai-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-machine-learning","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/tutorials\/alibaba-cloud-platform-for-ai-pai-tutorial-architecture-pricing-use-cases-and-hands-on-guide-for-ai-machine-learning\/","title":{"rendered":"Alibaba Cloud Platform For AI (PAI) Tutorial: Architecture, Pricing, Use Cases, and Hands-On Guide for AI &#038; Machine Learning"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Category<\/h2>\n\n\n\n<p>AI &amp; Machine Learning<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Introduction<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What this service is<\/h3>\n\n\n\n<p>Alibaba Cloud <strong>Platform For AI (PAI)<\/strong> is a managed <strong>AI &amp; Machine Learning<\/strong> platform used to build, train, evaluate, and operationalize machine learning and deep learning workloads on Alibaba Cloud infrastructure.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Simple explanation (one paragraph)<\/h3>\n\n\n\n<p>Think of Platform For AI (PAI) as a \u201cworkbench\u201d for data scientists and engineers: it provides managed notebooks, visual workflow tools, and training\/deployment capabilities so you can go from raw data to a working model without assembling everything yourself.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Technical explanation (one paragraph)<\/h3>\n\n\n\n<p>From a technical perspective, Platform For AI (PAI) is an integrated suite of services (and sub-products) that orchestrate compute (CPU\/GPU), storage, and data access for ML workflows. It typically integrates with Alibaba Cloud storage and data services (for example OSS and other data stores), supports VPC-based private networking, uses RAM for identity and access control, and provides managed development environments and training runtimes (exact options vary by region and account\u2014<strong>verify in official docs<\/strong>).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What problem it solves<\/h3>\n\n\n\n<p>PAI reduces the operational burden of ML by providing:\n&#8211; Repeatable environments for experiments (notebooks\/managed runtimes)\n&#8211; Scalable training without manually managing clusters\n&#8211; A clearer path to production (model packaging and deployment patterns)\n&#8211; Centralized governance (workspaces, permissions, audit, network controls)<\/p>\n\n\n\n<blockquote>\n<p>Naming note (important): In some older Alibaba Cloud materials you may still see <strong>\u201cMachine Learning Platform for AI (PAI)\u201d<\/strong> used as a longer form. Current English product naming commonly appears as <strong>Platform For AI (PAI)<\/strong>. <strong>Verify the exact current naming in your region\u2019s console and docs.<\/strong><\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">2. What is Platform For AI (PAI)?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Official purpose<\/h3>\n\n\n\n<p>Platform For AI (PAI) is Alibaba Cloud\u2019s managed platform in the <strong>AI &amp; Machine Learning<\/strong> category for building end-to-end ML workflows\u2014covering development, training, and (optionally) deployment\u2014using Alibaba Cloud resources.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Core capabilities (high level)<\/h3>\n\n\n\n<p>PAI commonly covers these capability areas (exact names\/features can vary\u2014verify in official docs):\n&#8211; <strong>Interactive development<\/strong>: managed notebook environments for exploration and prototyping\n&#8211; <strong>Pipeline\/workflow authoring<\/strong>: visual or structured workflows to run data processing + training + evaluation steps\n&#8211; <strong>Scalable training<\/strong>: single-node and distributed training with CPU\/GPU options\n&#8211; <strong>Model lifecycle operations<\/strong>: organizing model artifacts, versions, and promotion (capabilities vary by edition\/region)\n&#8211; <strong>Serving\/Inference<\/strong>: deploying models behind endpoints or for batch inference (if enabled\/available)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Major components (common PAI suite)<\/h3>\n\n\n\n<p>The PAI \u201cumbrella\u201d typically includes multiple functional sub-services. The most commonly referenced ones in Alibaba Cloud documentation include (names may appear with prefixes like PAI-; verify current product list in your console):\n&#8211; <strong>PAI-DSW (Data Science Workshop)<\/strong>: managed notebook-style development environments\n&#8211; <strong>PAI-Designer<\/strong>: visual pipeline design for ML workflows\n&#8211; <strong>PAI-DLC (Deep Learning Containers)<\/strong>: managed container-based training, including distributed training options\n&#8211; <strong>PAI-EAS (Elastic Algorithm Service)<\/strong>: model deployment\/serving and elastic inference (availability and supported runtimes vary\u2014verify)<\/p>\n\n\n\n<p>Other PAI family offerings may exist (for example recommendation or acceleration-related products). Treat them as related products unless your console explicitly lists them under Platform For AI (PAI).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Service type<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Managed AI platform<\/strong> (a suite of managed capabilities rather than a single API)<\/li>\n<li>Primarily <strong>control-plane managed<\/strong> by Alibaba Cloud; you pay for underlying compute\/storage\/network consumption based on the PAI modules you use (see Pricing section).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scope: regional\/global\/zonal and tenancy<\/h3>\n\n\n\n<p>In practice, PAI resources are typically <strong>region-scoped<\/strong> (you choose a region in the console and create resources there). Within a region, PAI commonly uses <strong>workspaces\/projects<\/strong> to isolate teams and manage permissions.<br\/>\nBecause exact scoping details can change by product edition and region, <strong>verify in official docs<\/strong>:\n&#8211; Whether workspaces are tied to an Alibaba Cloud account or resource directory\n&#8211; Whether a workspace can span multiple VPCs\n&#8211; Cross-region artifact access patterns (usually done via OSS replication or cross-region access)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How it fits into the Alibaba Cloud ecosystem<\/h3>\n\n\n\n<p>Platform For AI (PAI) is designed to work with:\n&#8211; <strong>RAM (Resource Access Management)<\/strong> for user\/role permissions and service access\n&#8211; <strong>VPC<\/strong> for network isolation, private access to data sources, and controlled egress\n&#8211; <strong>OSS (Object Storage Service)<\/strong> for datasets, checkpoints, and model artifacts\n&#8211; <strong>KMS<\/strong> (often used indirectly) for encryption key management (where supported)\n&#8211; <strong>Logging\/Audit<\/strong> services (for example ActionTrail for API auditing, and log services where integrated\u2014verify exact logging integration per module)<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">3. Why use Platform For AI (PAI)?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Business reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Faster time-to-model<\/strong>: teams can start training quickly without building a full ML platform.<\/li>\n<li><strong>Reduced platform engineering<\/strong>: managed notebooks and training reduce operational overhead.<\/li>\n<li><strong>Standardization<\/strong>: encourages consistent environments and repeatable workflows across teams.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Technical reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Elastic compute<\/strong>: scale CPU\/GPU resources up\/down for training bursts instead of permanent clusters.<\/li>\n<li><strong>Integrated data access<\/strong>: common patterns for working with OSS and private networks.<\/li>\n<li><strong>Workflow orchestration<\/strong>: reduces glue-code and manual steps when moving from data prep to training to evaluation.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Operational reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Separation of concerns<\/strong>: platform teams control networking\/IAM; data scientists focus on modeling.<\/li>\n<li><strong>Repeatability<\/strong>: workspace-based organization and pipeline definitions improve reproducibility.<\/li>\n<li><strong>Visibility<\/strong>: centralized place to track jobs, artifacts, and runs (feature depth varies\u2014verify).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Security\/compliance reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Least-privilege with RAM<\/strong>: grant workspace-level access aligned to team roles.<\/li>\n<li><strong>VPC isolation<\/strong>: run development and training in private networks and restrict outbound access.<\/li>\n<li><strong>Auditability<\/strong>: Alibaba Cloud auditing tools can record API actions and configuration changes.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scalability\/performance reasons<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Distributed training support<\/strong> (via PAI-DLC or equivalent) for large models and datasets.<\/li>\n<li><strong>GPU access<\/strong> for training acceleration and potentially inference.<\/li>\n<li><strong>Data locality<\/strong>: keep compute and data in the same region\/VPC to reduce latency and data transfer costs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">When teams should choose it<\/h3>\n\n\n\n<p>Choose Platform For AI (PAI) when you:\n&#8211; Need managed notebooks and training on Alibaba Cloud\n&#8211; Want to standardize ML workflows across teams\n&#8211; Expect sporadic but heavy compute usage (elastic scaling)\n&#8211; Need enterprise controls (IAM, VPC, auditing) in Alibaba Cloud<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When teams should not choose it<\/h3>\n\n\n\n<p>Avoid or reconsider PAI when:\n&#8211; You must deploy on-prem only, or on a different cloud with strict data residency that Alibaba Cloud cannot satisfy\n&#8211; You already have a mature ML platform (Kubeflow\/MLflow + Kubernetes) and PAI would duplicate capabilities\n&#8211; You require a specific framework\/runtime that PAI modules do not support in your region (<strong>verify supported runtimes<\/strong>)\n&#8211; Your budget model demands fixed-cost reserved infrastructure and you can run cheaper self-managed compute at scale (but weigh staffing\/ops costs)<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">4. Where is Platform For AI (PAI) used?<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Industries<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>E-commerce and retail (recommendations, demand forecasting)<\/li>\n<li>FinTech (fraud detection, credit risk modeling)<\/li>\n<li>Manufacturing (predictive maintenance, visual defect detection)<\/li>\n<li>Media and advertising (CTR prediction, content moderation pipelines)<\/li>\n<li>Logistics (route optimization, ETA prediction)<\/li>\n<li>Healthcare and life sciences (careful governance required; verify compliance needs)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Team types<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data science teams prototyping models<\/li>\n<li>ML engineering teams productionizing workflows<\/li>\n<li>Platform engineering teams providing shared ML infrastructure<\/li>\n<li>DevOps\/SRE teams operating ML training and serving environments<\/li>\n<li>Security teams enforcing network and IAM controls<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Workloads<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Supervised learning (classification\/regression)<\/li>\n<li>Deep learning training (vision\/NLP) with GPU<\/li>\n<li>Batch feature generation and offline scoring<\/li>\n<li>Model evaluation and periodic retraining<\/li>\n<li>Controlled notebook-based exploration on governed data<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Architectures<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data lake on OSS + training jobs in PAI<\/strong><\/li>\n<li><strong>VPC-private training<\/strong> connecting to databases or analytic platforms<\/li>\n<li><strong>CI\/CD for ML<\/strong> (often \u201cMLOps\u201d), integrating code repos and artifact storage (exact integrations vary)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Real-world deployment contexts<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Central \u201cAI platform\u201d shared by multiple product teams<\/li>\n<li>A single product team needing a low-ops ML environment<\/li>\n<li>Regulated environments using private VPC and strict RAM policies<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Production vs dev\/test usage<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dev\/Test<\/strong>: notebooks, small training runs, evaluation, feature exploration<\/li>\n<li><strong>Production<\/strong>: scheduled retraining pipelines, reproducible training environments, controlled artifact management, and model serving endpoints (if using PAI serving modules)<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">5. Top Use Cases and Scenarios<\/h2>\n\n\n\n<p>Below are realistic use cases where Alibaba Cloud Platform For AI (PAI) is commonly a good fit.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1) Notebook-based model prototyping<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Data scientists need a consistent environment to explore data and test models.<\/li>\n<li><strong>Why PAI fits<\/strong>: Managed notebook environments reduce setup time and provide controlled compute.<\/li>\n<li><strong>Example<\/strong>: A team uses a PAI notebook to test multiple feature sets for churn prediction using data stored in OSS.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2) Team workspaces and multi-tenant isolation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Multiple teams share a cloud account; they need separation and controlled access.<\/li>\n<li><strong>Why PAI fits<\/strong>: Workspaces\/projects (where supported) help isolate datasets, jobs, and permissions.<\/li>\n<li><strong>Example<\/strong>: Marketing and Risk teams each get their own PAI workspace with separate OSS prefixes and RAM policies.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3) Visual ML pipelines for repeatability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Manual, notebook-only workflows are hard to reproduce and operationalize.<\/li>\n<li><strong>Why PAI fits<\/strong>: Visual workflow\/pipeline tools can standardize preprocessing \u2192 training \u2192 evaluation steps.<\/li>\n<li><strong>Example<\/strong>: A fraud model pipeline runs nightly: feature generation, training, AUC evaluation, and artifact export.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4) Elastic training for periodic retraining<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Retraining only happens weekly\/monthly; dedicated clusters waste money.<\/li>\n<li><strong>Why PAI fits<\/strong>: Use on-demand CPU\/GPU for training windows, then shut down.<\/li>\n<li><strong>Example<\/strong>: A retailer retrains demand forecasts every weekend using temporary compute resources.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">5) GPU-accelerated deep learning training<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Training vision\/NLP models on CPU is too slow.<\/li>\n<li><strong>Why PAI fits<\/strong>: PAI training runtimes can use GPU instances (subject to region quotas).<\/li>\n<li><strong>Example<\/strong>: A QA team trains an image defect classifier using GPU-backed training jobs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6) Private network training connected to internal data sources<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Data resides in private subnets; public egress is not allowed.<\/li>\n<li><strong>Why PAI fits<\/strong>: VPC-based connectivity patterns enable private access to databases\/services.<\/li>\n<li><strong>Example<\/strong>: A bank trains models in a VPC that connects to private data services via VPC endpoints or private networking.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7) Batch scoring for offline predictions<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Need to score millions of records daily and store results for downstream systems.<\/li>\n<li><strong>Why PAI fits<\/strong>: Training + batch prediction can be orchestrated as jobs\/workflows.<\/li>\n<li><strong>Example<\/strong>: A logistics company produces daily ETA predictions and saves them as OSS files for reporting.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">8) Feature engineering at scale (where integrated with data processing)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Feature generation is heavy and must be consistent across training and scoring.<\/li>\n<li><strong>Why PAI fits<\/strong>: Pipeline steps can standardize feature computation and reuse.<\/li>\n<li><strong>Example<\/strong>: A marketplace builds and version-controls feature sets used by both training and batch scoring.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">9) Model evaluation and governance gates<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Models must pass metrics and checks before production use.<\/li>\n<li><strong>Why PAI fits<\/strong>: Workflow steps can enforce evaluation thresholds and export only passing artifacts.<\/li>\n<li><strong>Example<\/strong>: A credit scoring model is exported only if AUC and stability checks meet thresholds.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">10) Standardized environments for education and onboarding<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Training new hires requires consistent environments and datasets.<\/li>\n<li><strong>Why PAI fits<\/strong>: Notebooks and workspaces offer repeatable labs.<\/li>\n<li><strong>Example<\/strong>: A company onboarding program uses a PAI workspace with curated datasets and exercises.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">11) Multi-model experimentation with controlled costs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Teams want to experiment but avoid uncontrolled GPU spending.<\/li>\n<li><strong>Why PAI fits<\/strong>: Workspace quotas and instance selection help control spend (where supported).<\/li>\n<li><strong>Example<\/strong>: A team uses small CPU notebooks for EDA and only spins GPU for final training runs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">12) Pre-production \u201cshadow\u201d inference testing (optional serving)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: Validate inference latency\/accuracy on real traffic without impacting production.<\/li>\n<li><strong>Why PAI fits<\/strong>: If using PAI serving modules, can deploy a parallel endpoint and compare.<\/li>\n<li><strong>Example<\/strong>: A recommendation model is deployed to a staging endpoint to evaluate performance and latency before promotion.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">6. Core Features<\/h2>\n\n\n\n<blockquote>\n<p>Note: Platform For AI (PAI) is a suite. Some capabilities depend on which PAI module you enable (for example notebook vs training vs serving). If a feature name differs in your region, use the closest matching module and <strong>verify in official docs<\/strong>.<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">1) Workspaces \/ projects (team isolation)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Organizes users, jobs, and artifacts by workspace\/project.<\/li>\n<li><strong>Why it matters<\/strong>: Reduces accidental cross-team access and simplifies governance.<\/li>\n<li><strong>Practical benefit<\/strong>: Separate dev\/test\/prod workspaces; map teams to least-privilege RAM policies.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Cross-workspace sharing can be non-trivial; plan OSS paths and RAM policies carefully.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2) Managed notebook environments (commonly PAI-DSW)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Provides browser-based interactive compute for Python\/R and ML workflows.<\/li>\n<li><strong>Why it matters<\/strong>: Removes friction of setting up environments, packages, and compute.<\/li>\n<li><strong>Practical benefit<\/strong>: Quickly run experiments on scalable CPU\/GPU instances.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Notebooks are great for exploration but need discipline for production; enforce code repository usage and environment pinning.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3) Visual workflow \/ pipeline design (commonly PAI-Designer)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Build pipelines by connecting components for data preprocessing, training, evaluation, and output.<\/li>\n<li><strong>Why it matters<\/strong>: Encourages reproducible workflows and reduces manual steps.<\/li>\n<li><strong>Practical benefit<\/strong>: Non-experts can run standard workflows; easier handoff to ops teams.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Visual pipelines can hide complexity; ensure version control of configurations and input data snapshots.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4) Managed training with containers (commonly PAI-DLC)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Runs training jobs in managed container environments, potentially distributed.<\/li>\n<li><strong>Why it matters<\/strong>: Scales training without building Kubernetes orchestration yourself.<\/li>\n<li><strong>Practical benefit<\/strong>: Use standardized images\/runtimes for consistent results.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Container image compatibility and framework versions must be validated; GPU availability varies by region\/quota.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">5) Elastic inference \/ model serving (commonly PAI-EAS)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Hosts models behind an endpoint with autoscaling (capabilities vary).<\/li>\n<li><strong>Why it matters<\/strong>: Enables production inference without managing servers manually.<\/li>\n<li><strong>Practical benefit<\/strong>: Deploy models for online prediction, manage traffic, and scale with demand.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Supported model formats\/frameworks and deployment patterns vary\u2014<strong>verify supported runtimes and deployment specs<\/strong> before committing.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">6) Integration with OSS for datasets and artifacts<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Uses OSS as a durable store for training data, checkpoints, model files, and outputs.<\/li>\n<li><strong>Why it matters<\/strong>: Separates ephemeral compute from persistent assets.<\/li>\n<li><strong>Practical benefit<\/strong>: Easier reproducibility and cross-job artifact reuse.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Large data transfers can cost money and time; keep compute in the same region as OSS.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">7) VPC networking and private access patterns<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Allows running notebooks\/training with VPC attachment (where supported).<\/li>\n<li><strong>Why it matters<\/strong>: Keeps data and traffic private; supports compliance controls.<\/li>\n<li><strong>Practical benefit<\/strong>: Access private data sources without exposing them to the internet.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Requires careful subnet\/route\/Security Group design; egress control often needs NAT\/proxy.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">8) RAM-based access control<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Controls who can create jobs, attach OSS, and manage deployments.<\/li>\n<li><strong>Why it matters<\/strong>: Prevents unauthorized access to sensitive datasets and compute.<\/li>\n<li><strong>Practical benefit<\/strong>: Enforce least privilege; separate \u201cdata reader\u201d, \u201ctrainer\u201d, \u201cadmin\u201d roles.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Mis-scoped OSS permissions are a common cause of leaks; audit regularly.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">9) Job\/run monitoring and logs (module-dependent)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Provides job status, metrics, and logs for debugging and operations.<\/li>\n<li><strong>Why it matters<\/strong>: You need visibility into failures, resource usage, and runtime behavior.<\/li>\n<li><strong>Practical benefit<\/strong>: Faster troubleshooting; easier SRE handoff.<\/li>\n<li><strong>Limitations\/caveats<\/strong>: Centralized logging integration varies; you may need to forward logs to Alibaba Cloud logging services (verify).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">10) Resource\/compute management (instance types, quotas, queues)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>What it does<\/strong>: Lets you choose compute shapes (CPU\/GPU\/memory) and manage quotas.<\/li>\n<li><strong>Why it matters<\/strong>: Controls performance and cost.<\/li>\n<li><strong>Practical benefit<\/strong>: Right-size compute for each stage (EDA vs training vs evaluation).<\/li>\n<li><strong>Limitations\/caveats<\/strong>: GPU quotas can block scaling; plan capacity and request quota increases early.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">7. Architecture and How It Works<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">High-level architecture<\/h3>\n\n\n\n<p>Platform For AI (PAI) typically follows a control-plane\/data-plane model:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Control plane<\/strong>: PAI console and APIs manage workspaces, job definitions, deployments, permissions, and metadata.<\/li>\n<li><strong>Data plane<\/strong>: Compute (notebooks\/training jobs\/inference) runs in your selected region, reading datasets from OSS or other data sources, and writing artifacts back to OSS.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Request\/data\/control flow (typical)<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>User authenticates to Alibaba Cloud (RAM user\/role) and opens PAI in a region.<\/li>\n<li>User selects a workspace and creates a notebook or training job.<\/li>\n<li>Compute is provisioned (CPU\/GPU) in the region (and optionally inside a VPC).<\/li>\n<li>Job reads training data from OSS (and\/or other sources reachable via the network).<\/li>\n<li>Job writes outputs: logs, metrics, model artifacts to OSS or workspace storage.<\/li>\n<li>Optional: a serving module deploys the model for online inference.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Common integrations with related services (Alibaba Cloud)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>OSS<\/strong>: datasets and artifacts<\/li>\n<li><strong>VPC<\/strong>: private networking for compute<\/li>\n<li><strong>RAM<\/strong>: authentication and authorization<\/li>\n<li><strong>ActionTrail<\/strong>: audit of API actions (for governance)<\/li>\n<li><strong>NAT Gateway \/ EIP<\/strong>: controlled outbound access (when private subnets need internet access)<\/li>\n<li><strong>KMS<\/strong>: encryption key management (where supported by OSS and other services)<\/li>\n<\/ul>\n\n\n\n<p>Because PAI is a suite, the exact integration points depend on which PAI module you use. Always check module-specific documentation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Dependency services<\/h3>\n\n\n\n<p>At minimum, most PAI workflows depend on:\n&#8211; An Alibaba Cloud account with billing enabled\n&#8211; OSS for persistent data\/artifacts (highly recommended)\n&#8211; Proper RAM permissions\n&#8211; Optional but common: VPC and related networking components for private access<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Security\/authentication model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Users and services authenticate with <strong>RAM identities<\/strong>.<\/li>\n<li>Jobs and notebooks typically need permission to read\/write OSS paths.<\/li>\n<li>Cross-service access is usually done via RAM roles\/policies (for example, granting PAI runtime permission to access an OSS bucket\/prefix). Exact mechanism depends on module\u2014<strong>verify<\/strong>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Networking model<\/h3>\n\n\n\n<p>Typical patterns:\n&#8211; <strong>Public access<\/strong>: easiest setup for beginners; compute can access internet (riskier).\n&#8211; <strong>VPC attached<\/strong>: notebooks\/training run in a VPC; access private resources; optionally restrict outbound internet via NAT\/proxy.\n&#8211; <strong>Hybrid<\/strong>: private data plane with controlled egress to pull packages\/images.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Monitoring\/logging\/governance considerations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Track:<\/li>\n<li>job execution status and failures<\/li>\n<li>resource consumption (CPU\/GPU utilization where visible)<\/li>\n<li>OSS access patterns and denied requests (indicates permission issues)<\/li>\n<li>Use:<\/li>\n<li><strong>ActionTrail<\/strong> for auditing management actions (who created jobs, modified settings)<\/li>\n<li>module-specific logs; forward to centralized logging if available\/needed (verify)<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Simple architecture diagram (Mermaid)<\/h3>\n\n\n\n<pre><code class=\"language-mermaid\">flowchart LR\n  U[User (RAM User)] --&gt; C[PAI Console \/ API]\n  C --&gt; WS[PAI Workspace]\n  WS --&gt; NB[Notebook \/ Training Job]\n  NB &lt;--&gt; OSS[OSS Bucket (Data + Artifacts)]\n  NB --&gt; OUT[Model Files + Metrics]\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Production-style architecture diagram (Mermaid)<\/h3>\n\n\n\n<pre><code class=\"language-mermaid\">flowchart TB\n  subgraph Identity[Identity &amp; Governance]\n    RAM[RAM Users\/Roles\/Policies]\n    AT[ActionTrail (Audit)]\n  end\n\n  subgraph Network[VPC Network]\n    VPC[VPC]\n    SUB[Private Subnets]\n    SG[Security Groups]\n    NAT[NAT Gateway (optional egress control)]\n  end\n\n  subgraph Data[Data Layer]\n    OSS[(OSS: Datasets, Checkpoints, Models)]\n    DS[Private Data Sources\\n(DB\/Analytics - verify)]\n  end\n\n  subgraph PAI[Alibaba Cloud Platform For AI (PAI)]\n    WS[Workspace\/Project]\n    DSW[Managed Notebook (PAI-DSW)]\n    DLC[Training Jobs (PAI-DLC)]\n    PIPE[Workflow\/Pipeline (PAI-Designer)]\n    SERVE[Model Serving (PAI-EAS - optional)]\n  end\n\n  RAM --&gt; PAI\n  AT --&gt; PAI\n\n  WS --&gt; DSW\n  WS --&gt; PIPE\n  PIPE --&gt; DLC\n  DSW &lt;--&gt; OSS\n  DLC &lt;--&gt; OSS\n\n  DSW --- SUB\n  DLC --- SUB\n  SUB --- SG\n  SUB --- NAT\n\n  SUB --&gt; DS\n  SERVE --&gt; SUB\n  SERVE --&gt; OSS\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">8. Prerequisites<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Account \/ billing<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>An active <strong>Alibaba Cloud account<\/strong> with <strong>billing enabled<\/strong> (pay-as-you-go is common for PAI usage).<\/li>\n<li>If your organization uses <strong>Resource Directory<\/strong> or multi-account governance, confirm where PAI workspaces should live (<strong>verify organizational setup<\/strong>).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Permissions (IAM \/ RAM)<\/h3>\n\n\n\n<p>At minimum, you need permissions to:\n&#8211; Access Platform For AI (PAI) in the target region\n&#8211; Create and manage a PAI workspace (or be granted access to an existing workspace)\n&#8211; Create notebook instances and\/or training jobs\n&#8211; Read\/write to OSS buckets\/prefixes used for data and artifacts<\/p>\n\n\n\n<p>Practical guidance:\n&#8211; Prefer a <strong>RAM user<\/strong> or <strong>RAM role<\/strong> with least-privilege access.\n&#8211; If you don\u2019t control account-wide IAM, ask for a workspace-scoped role and OSS access to a dedicated bucket\/prefix.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Tools needed<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Alibaba Cloud Console access (web browser)<\/li>\n<li>Optional: <strong>aliyun CLI<\/strong> for OSS and automation (helpful but not required)<\/li>\n<li>Official CLI docs: https:\/\/www.alibabacloud.com\/help\/en\/cli<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Region availability<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Choose a region where PAI is available and where your data will reside.<\/li>\n<li>GPU instance availability varies significantly by region.<\/li>\n<li>Always verify PAI module availability (DSW\/DLC\/EAS\/Designer) in your chosen region.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Quotas \/ limits<\/h3>\n\n\n\n<p>Common quota categories (exact quota names vary\u2014verify):\n&#8211; Max number of notebook instances\n&#8211; CPU\/GPU quota for training jobs\n&#8211; Concurrent job limits\n&#8211; Storage limits for workspace-managed storage (if any)\n&#8211; OSS request limits (service-level) and bucket policy constraints<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisite services<\/h3>\n\n\n\n<p>For this tutorial, you should have:\n&#8211; <strong>OSS<\/strong> available in the same region (recommended)\n&#8211; Optional but recommended for production-like isolation: <strong>VPC<\/strong>, subnets, and security groups<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">9. Pricing \/ Cost<\/h2>\n\n\n\n<blockquote>\n<p>Pricing for Platform For AI (PAI) is not usually a single flat fee. It is typically the sum of the resources consumed by the PAI modules you use (compute, storage, networking, and sometimes platform features). Exact pricing is <strong>region-dependent<\/strong> and <strong>module-dependent<\/strong>\u2014use official pricing pages and your Alibaba Cloud billing console.<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">Official pricing sources (start here)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Product entry point for PAI (contains docs and links): https:\/\/www.alibabacloud.com\/help\/en\/pai\/<\/li>\n<li>Alibaba Cloud pricing overview: https:\/\/www.alibabacloud.com\/pricing  <\/li>\n<li>OSS pricing (often a major component): https:\/\/www.alibabacloud.com\/product\/oss#pricing (verify current URL\/region selector)<\/li>\n<\/ul>\n\n\n\n<p>If your account has access to a pricing calculator for your region, use it. If not, rely on the billing console and module-specific \u201cBilling\u201d documentation pages (search within PAI docs for \u201cbilling\u201d).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Pricing dimensions (typical)<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Compute for notebooks (PAI-DSW)<\/strong><br\/>\n   &#8211; Billed by instance type (CPU\/GPU, RAM), and runtime duration.<\/li>\n<li><strong>Compute for training jobs (PAI-DLC \/ training module)<\/strong><br\/>\n   &#8211; Billed by workers\/instances, instance type, runtime duration, and possibly storage attached to jobs.<\/li>\n<li><strong>Inference\/serving (PAI-EAS, if used)<\/strong><br\/>\n   &#8211; Billed by instances (and autoscaling min\/max), runtime hours, and possibly network traffic.<\/li>\n<li><strong>Storage (OSS)<\/strong><br\/>\n   &#8211; Billed by stored GB-month, requests, and outbound traffic.<\/li>\n<li><strong>Network<\/strong><br\/>\n   &#8211; Internet egress charges, NAT Gateway, EIP bandwidth, cross-zone\/cross-region transfer (where applicable).<\/li>\n<li><strong>Logging\/monitoring<\/strong> (if forwarding logs to a paid logging service)<br\/>\n   &#8211; Ingestion, indexing, retention (service-dependent\u2014verify).<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Free tier<\/h3>\n\n\n\n<p>Alibaba Cloud free tiers\/promotions vary by region and time. Do not assume a free tier exists for PAI modules. Check:\n&#8211; https:\/\/www.alibabacloud.com\/free (verify current offers)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Main cost drivers<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>GPU instance selection and runtime (largest driver for DL)<\/li>\n<li>Idle notebook instances left running<\/li>\n<li>Training jobs with large worker counts or long durations<\/li>\n<li>OSS storage growth (datasets + repeated checkpoints)<\/li>\n<li>Internet egress (downloading datasets\/models out of Alibaba Cloud)<\/li>\n<li>NAT Gateway + EIP bandwidth (if using private VPC with controlled egress)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Hidden or indirect costs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Repeated artifacts<\/strong>: storing multiple versions of large checkpoints in OSS can quietly grow costs.<\/li>\n<li><strong>Data duplication<\/strong>: copying the same dataset into multiple buckets\/regions multiplies storage + transfer costs.<\/li>\n<li><strong>Package\/image downloads<\/strong>: repeated container pulls or pip installs can add time (and sometimes network costs).<\/li>\n<li><strong>Logging retention<\/strong>: long retention at high volume can become a material cost.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Network\/data transfer implications<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Keep compute and OSS in the <strong>same region<\/strong> to minimize latency and cross-region costs.<\/li>\n<li>Minimize <strong>public egress<\/strong> by:<\/li>\n<li>Using VPC endpoints\/private connectivity where available<\/li>\n<li>Keeping downstream consumers in-region<\/li>\n<li>Downloading artifacts only when needed<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Cost optimization tips (practical)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Shut down notebook instances when not in use (or enforce auto-stop if available).<\/li>\n<li>Start with CPU for EDA; switch to GPU only when needed.<\/li>\n<li>Right-size instances (avoid \u201clargest instance by default\u201d).<\/li>\n<li>Use lifecycle rules in OSS to transition old artifacts to lower-cost storage classes (if appropriate).<\/li>\n<li>Version artifacts intentionally (keep \u201cblessed\u201d models; prune intermediates).<\/li>\n<li>Set workspace budgets\/alerts in the billing console.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Example low-cost starter estimate (no fabricated numbers)<\/h3>\n\n\n\n<p>A typical beginner lab might include:\n&#8211; One small CPU notebook for 1\u20133 hours\n&#8211; A few GB in OSS for dataset + model artifacts\n&#8211; Minimal or no internet egress (keep everything in Alibaba Cloud)<\/p>\n\n\n\n<p>Your total cost depends on your chosen region and instance type. Expect compute to dominate even in small labs. <strong>Check the hourly rate for the notebook instance type in your region and multiply by expected hours<\/strong>, then add OSS storage and request costs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Example production cost considerations<\/h3>\n\n\n\n<p>For a production system, plan for:\n&#8211; Separate dev\/test\/prod environments (multiplies baseline)\n&#8211; Regular retraining schedules (weekly\/daily)\n&#8211; GPU training bursts + potential serving 24\/7 (if using online inference)\n&#8211; Observability and audit retention requirements\n&#8211; Data growth: datasets, feature sets, training logs, artifacts<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">10. Step-by-Step Hands-On Tutorial<\/h2>\n\n\n\n<p>This lab focuses on a realistic, low-risk workflow that is executable without assuming advanced serving features: <strong>train a small model in a managed notebook, save artifacts, and persist them to OSS<\/strong>. This gives you a solid foundation for production patterns (artifact storage, repeatability, and cleanup).<\/p>\n\n\n\n<blockquote>\n<p>If your account\/region includes PAI deployment\/serving modules (for example PAI-EAS), you can extend this lab later. Serving specifics vary\u2014<strong>verify in official docs<\/strong>.<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">Objective<\/h3>\n\n\n\n<p>Use <strong>Alibaba Cloud Platform For AI (PAI)<\/strong> to:\n1. Create a workspace\n2. Launch a managed notebook environment\n3. Train a simple ML model\n4. Save the model artifact and upload it to OSS\n5. Validate the artifact is stored correctly\n6. Clean up resources to avoid ongoing charges<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Lab Overview<\/h3>\n\n\n\n<p>You will:\n&#8211; Create an OSS bucket (or reuse an existing one)\n&#8211; Create a PAI workspace\n&#8211; Start a notebook instance (PAI-DSW or the notebook module available in your console)\n&#8211; Run Python code to train a model on a small dataset\n&#8211; Save the model file locally and upload it to OSS\n&#8211; Confirm OSS contains the artifact\n&#8211; Stop\/delete the notebook instance and optionally delete OSS objects<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1: Choose a region and create (or identify) an OSS bucket<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>In the Alibaba Cloud Console, select a <strong>region<\/strong> where Platform For AI (PAI) is available.<\/li>\n<li>Go to <strong>Object Storage Service (OSS)<\/strong>.<\/li>\n<li>Create a bucket (or choose an existing one):\n   &#8211; Keep the bucket in the <strong>same region<\/strong> as your PAI workspace.\n   &#8211; For a lab, keep settings simple.\n   &#8211; For production, prefer private buckets, encryption, and least-privilege policies.<\/li>\n<\/ol>\n\n\n\n<p><strong>Expected outcome<\/strong>\n&#8211; You have an OSS bucket name and a dedicated prefix\/folder for this lab, for example:\n  &#8211; <code>oss:\/\/my-ml-bucket\/pai-labs\/model-artifacts\/<\/code><\/p>\n\n\n\n<p><strong>Verification<\/strong>\n&#8211; In OSS console, confirm the bucket exists and is accessible.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2: Create a Platform For AI (PAI) workspace<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Open <strong>Platform For AI (PAI)<\/strong> in the Alibaba Cloud Console.<\/li>\n<li>Create a <strong>workspace<\/strong> (or project):\n   &#8211; Name: <code>pai-lab-workspace<\/code>\n   &#8211; Description: optional\n   &#8211; Configure access control as required (for a solo lab, you can grant yourself admin within the workspace).<\/li>\n<\/ol>\n\n\n\n<p><strong>Expected outcome<\/strong>\n&#8211; Workspace is created and visible in the PAI console.<\/p>\n\n\n\n<p><strong>Verification<\/strong>\n&#8211; Open the workspace and confirm you can access notebook\/training features.<\/p>\n\n\n\n<p><strong>Common issue<\/strong>\n&#8211; You can\u2019t create a workspace due to permissions.<br\/>\n<strong>Fix<\/strong>: Ask an account admin to grant your RAM user the required PAI permissions and OSS access.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3: Create a managed notebook instance (PAI notebook\/DSW)<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>In the PAI workspace, locate the notebook feature (commonly labeled <strong>DSW<\/strong>, <strong>Notebook<\/strong>, or <strong>Data Science Workshop<\/strong>\u2014naming varies).<\/li>\n<li>\n<p>Create a new notebook instance:\n   &#8211; Start with a <strong>small CPU<\/strong> instance type to control cost.\n   &#8211; Select an environment image that includes Python (common).\n   &#8211; If there is a \u201cVPC\u201d option and you are not testing private networking yet, you can start without VPC to keep the lab simpler. For production, prefer VPC.<\/p>\n<\/li>\n<li>\n<p>Launch the notebook and open Jupyter (or the integrated IDE).<\/p>\n<\/li>\n<\/ol>\n\n\n\n<p><strong>Expected outcome<\/strong>\n&#8211; You have an interactive notebook session running.<\/p>\n\n\n\n<p><strong>Verification<\/strong>\n&#8211; Run a basic Python cell:\n  <code>python\n  import sys\n  print(sys.version)<\/code><\/p>\n\n\n\n<p><strong>Cost control tip<\/strong>\n&#8211; If the notebook supports auto-stop\/idle shutdown, enable it.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4: Train a small model and save the artifact locally<\/h3>\n\n\n\n<p>In a new notebook cell, run the following Python code. It trains a simple model using scikit-learn, evaluates it, and saves the model with <code>joblib<\/code>.<\/p>\n\n\n\n<pre><code class=\"language-python\">import os\nimport joblib\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score, classification_report\n\n# 1) Load data\niris = load_iris()\nX = iris.data\ny = iris.target\n\n# 2) Split\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.2, random_state=42, stratify=y\n)\n\n# 3) Build a simple pipeline\nclf = Pipeline(steps=[\n    (\"scaler\", StandardScaler()),\n    (\"model\", LogisticRegression(max_iter=200))\n])\n\n# 4) Train\nclf.fit(X_train, y_train)\n\n# 5) Evaluate\npred = clf.predict(X_test)\nacc = accuracy_score(y_test, pred)\nprint(\"Accuracy:\", acc)\nprint(classification_report(y_test, pred, target_names=iris.target_names))\n\n# 6) Save model\nout_dir = \"artifacts\"\nos.makedirs(out_dir, exist_ok=True)\nmodel_path = os.path.join(out_dir, \"iris_logreg.joblib\")\njoblib.dump(clf, model_path)\n\nprint(\"Saved model to:\", model_path)\nprint(\"File size (bytes):\", os.path.getsize(model_path))\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>\n&#8211; You see an accuracy score (typically high for Iris).\n&#8211; A file exists at <code>artifacts\/iris_logreg.joblib<\/code>.<\/p>\n\n\n\n<p><strong>Verification<\/strong>\nRun:<\/p>\n\n\n\n<pre><code class=\"language-python\">import os\nassert os.path.exists(\"artifacts\/iris_logreg.joblib\")\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5: Upload the artifact to OSS<\/h3>\n\n\n\n<p>There are multiple ways to upload to OSS. Choose the one that matches what is available in your notebook environment.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Option A (recommended if available): Use <code>ossutil<\/code><\/h4>\n\n\n\n<p>Many Alibaba Cloud environments use <code>ossutil<\/code>\/<code>ossutil64<\/code>. In a notebook terminal:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p>Check whether ossutil exists:\n   <code>bash\n   which ossutil || which ossutil64<\/code><\/p>\n<\/li>\n<li>\n<p>If present, configure it (you may need AccessKey credentials\u2014avoid long-lived keys in production; prefer RAM roles where supported). Configuration steps vary\u2014<strong>verify in official ossutil docs<\/strong>:\n   &#8211; https:\/\/www.alibabacloud.com\/help\/en\/oss\/developer-reference\/ossutil<\/p>\n<\/li>\n<li>\n<p>Upload the model:\n   <code>bash\n   ossutil cp artifacts\/iris_logreg.joblib oss:\/\/YOUR_BUCKET\/pai-labs\/model-artifacts\/iris_logreg.joblib<\/code><\/p>\n<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\">Option B: Use the OSS Python SDK (<code>oss2<\/code>)<\/h4>\n\n\n\n<p>If you cannot use ossutil, you can use Python. This typically requires AccessKey ID\/Secret or a role-based credential provider. For a lab, you may use temporary credentials if your org provides them. <strong>Do not hardcode keys in notebooks for production.<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p>Install:\n   <code>python\n   !pip -q install oss2<\/code><\/p>\n<\/li>\n<li>\n<p>Upload (example skeleton\u2014<strong>verify credential method in official OSS SDK docs<\/strong>):\n   &#8211; OSS Python SDK docs: https:\/\/www.alibabacloud.com\/help\/en\/oss\/developer-reference\/python<\/p>\n<\/li>\n<\/ol>\n\n\n\n<pre><code class=\"language-python\">import oss2\nimport os\n\n# Fill these in via environment variables or a secure method.\n# For production, prefer RAM role-based auth if supported in your runtime.\nendpoint = os.environ.get(\"OSS_ENDPOINT\")      # e.g., \"https:\/\/oss-cn-&lt;region&gt;.aliyuncs.com\"\nbucket_name = os.environ.get(\"OSS_BUCKET_NAME\")\naccess_key_id = os.environ.get(\"ALIBABA_CLOUD_ACCESS_KEY_ID\")\naccess_key_secret = os.environ.get(\"ALIBABA_CLOUD_ACCESS_KEY_SECRET\")\n\nassert endpoint and bucket_name and access_key_id and access_key_secret, \"Set OSS env vars first.\"\n\nauth = oss2.Auth(access_key_id, access_key_secret)\nbucket = oss2.Bucket(auth, endpoint, bucket_name)\n\nlocal_file = \"artifacts\/iris_logreg.joblib\"\noss_key = \"pai-labs\/model-artifacts\/iris_logreg.joblib\"\n\nbucket.put_object_from_file(oss_key, local_file)\nprint(\"Uploaded to OSS key:\", oss_key)\n<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">Option C: Download locally and upload via OSS Console<\/h4>\n\n\n\n<p>If you cannot configure CLI\/SDK:\n1. Download <code>artifacts\/iris_logreg.joblib<\/code> to your laptop from the notebook UI.\n2. Upload it through the OSS console to the intended bucket\/prefix.<\/p>\n\n\n\n<p><strong>Expected outcome<\/strong>\n&#8211; The model artifact is stored in OSS at your chosen key\/prefix.<\/p>\n\n\n\n<p><strong>Verification<\/strong>\n&#8211; In OSS Console, browse to:\n  &#8211; <code>pai-labs\/model-artifacts\/iris_logreg.joblib<\/code>\n&#8211; Confirm object size is non-zero.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Step 6: Load the model back (sanity test)<\/h3>\n\n\n\n<p>This step confirms the artifact is usable.<\/p>\n\n\n\n<pre><code class=\"language-python\">import joblib\nimport numpy as np\n\nmodel = joblib.load(\"artifacts\/iris_logreg.joblib\")\nsample = np.array([[5.1, 3.5, 1.4, 0.2]])\nprint(\"Predicted class:\", model.predict(sample))\nprint(\"Predicted probs:\", model.predict_proba(sample))\n<\/code><\/pre>\n\n\n\n<p><strong>Expected outcome<\/strong>\n&#8211; You see a predicted class (0\/1\/2) and probabilities.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Validation<\/h3>\n\n\n\n<p>You have successfully completed the lab if:\n&#8211; A PAI workspace exists (or you used an existing one)\n&#8211; A managed notebook instance ran your training code\n&#8211; A model artifact file was created locally\n&#8211; The artifact was uploaded to OSS and is visible in the OSS console\n&#8211; The artifact can be loaded and used for prediction in Python<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Troubleshooting<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Issue: \u201cAccessDenied\u201d when uploading to OSS<\/h4>\n\n\n\n<p><strong>Cause<\/strong>\n&#8211; Your RAM identity lacks OSS permissions (bucket policy, RAM policy, or wrong region endpoint).<\/p>\n\n\n\n<p><strong>Fix<\/strong>\n&#8211; Confirm the bucket name and region endpoint match.\n&#8211; Ask an admin to grant least-privilege permissions to:\n  &#8211; <code>oss:PutObject<\/code> on the target prefix\n  &#8211; <code>oss:GetObject<\/code> for reading back\n&#8211; Verify any bucket policy restrictions.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Issue: Notebook cannot install packages (<code>pip<\/code> fails)<\/h4>\n\n\n\n<p><strong>Cause<\/strong>\n&#8211; No internet egress (common in VPC-private environments), or DNS\/proxy restrictions.<\/p>\n\n\n\n<p><strong>Fix<\/strong>\n&#8211; If in a private VPC, configure NAT\/proxy for controlled egress.\n&#8211; Use prebuilt images that already include required libraries.\n&#8211; Use an internal mirror\/artifact repository (enterprise pattern).<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Issue: Notebook left running and costs increase<\/h4>\n\n\n\n<p><strong>Cause<\/strong>\n&#8211; Instances continue billing while running.<\/p>\n\n\n\n<p><strong>Fix<\/strong>\n&#8211; Stop\/shutdown notebook when idle.\n&#8211; Enable auto-stop\/idle shutdown if available.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Issue: GPU not available \/ cannot select GPU instance<\/h4>\n\n\n\n<p><strong>Cause<\/strong>\n&#8211; GPU quota not available in region, or instance stock is limited.<\/p>\n\n\n\n<p><strong>Fix<\/strong>\n&#8211; Try another region, request quota increase, or run CPU for this lab.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h3 class=\"wp-block-heading\">Cleanup<\/h3>\n\n\n\n<p>To avoid ongoing charges:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\n<p><strong>Stop or delete the notebook instance<\/strong>\n   &#8211; In PAI console, stop\/shutdown the notebook.\n   &#8211; If you don\u2019t need it, delete it.<\/p>\n<\/li>\n<li>\n<p><strong>Delete artifacts in OSS (optional)<\/strong>\n   &#8211; Delete <code>pai-labs\/model-artifacts\/iris_logreg.joblib<\/code> and any lab objects.<\/p>\n<\/li>\n<li>\n<p><strong>Delete the workspace (optional)<\/strong>\n   &#8211; If this workspace was only for the lab and your org allows deletion.<\/p>\n<\/li>\n<li>\n<p><strong>Check Billing<\/strong>\n   &#8211; Review current usage and ensure there are no running instances or deployments.<\/p>\n<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">11. Best Practices<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Architecture best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Keep <strong>data, compute, and artifacts in the same region<\/strong> to reduce latency and transfer costs.<\/li>\n<li>Separate environments:<\/li>\n<li>dev workspace (experimentation)<\/li>\n<li>staging workspace (pipeline hardening)<\/li>\n<li>prod workspace (locked-down, controlled deployments)<\/li>\n<li>Standardize artifact paths in OSS:<\/li>\n<li><code>oss:\/\/bucket\/ml\/&lt;team&gt;\/&lt;project&gt;\/&lt;env&gt;\/&lt;model&gt;\/&lt;version&gt;\/<\/code><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">IAM\/security best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use <strong>least privilege<\/strong> RAM policies.<\/li>\n<li>Avoid long-lived AccessKeys in notebooks. Prefer:<\/li>\n<li>RAM roles (where supported)<\/li>\n<li>temporary credentials (STS) for short-lived access<\/li>\n<li>Restrict OSS bucket access with:<\/li>\n<li>bucket policies limited to required prefixes<\/li>\n<li>private buckets by default<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Cost best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enforce notebook auto-stop policies if available.<\/li>\n<li>Right-size instances and use smaller compute for EDA.<\/li>\n<li>Clean up intermediate artifacts and old checkpoints.<\/li>\n<li>Use OSS lifecycle rules for aging data (transition to cheaper classes when appropriate).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Performance best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Place OSS and compute in the same region.<\/li>\n<li>Use parallel data loading where appropriate (within framework best practices).<\/li>\n<li>For large datasets, design input pipelines that avoid small-file storms in OSS.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Reliability best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Store all important artifacts in OSS (not only on notebook disk).<\/li>\n<li>Capture environment details (Python version, package versions, image ID).<\/li>\n<li>Make pipelines idempotent and re-runnable.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Operations best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Centralize logs where possible; define retention policies.<\/li>\n<li>Use naming conventions for jobs, datasets, and artifacts.<\/li>\n<li>Monitor job failures and set alerting thresholds (service-dependent).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Governance\/tagging\/naming best practices<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use consistent naming:<\/li>\n<li><code>team-project-env-purpose<\/code><\/li>\n<li>Apply tags on related cloud resources (OSS bucket tags, compute tags if supported).<\/li>\n<li>Maintain an internal \u201cmodel registry\u201d record even if it\u2019s initially a simple table documenting:<\/li>\n<li>model version, training data snapshot, metrics, owner, approval date<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">12. Security Considerations<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Identity and access model<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use <strong>RAM<\/strong> for:<\/li>\n<li>user authentication (console\/API)<\/li>\n<li>service authorization (PAI access + OSS access)<\/li>\n<li>Prefer role-based access aligned to job functions:<\/li>\n<li>Data Scientist: run notebook\/jobs, read curated datasets, write artifacts<\/li>\n<li>ML Engineer: manage pipelines, promote artifacts<\/li>\n<li>Admin: manage workspace settings and networking<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Encryption<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>At rest<\/strong>:<\/li>\n<li>OSS supports server-side encryption options (including KMS-backed options depending on configuration\u2014verify in OSS docs).<\/li>\n<li><strong>In transit<\/strong>:<\/li>\n<li>Use HTTPS endpoints for OSS access.<\/li>\n<li>Keep internal traffic inside VPC where possible.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Network exposure<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Prefer <strong>VPC-attached<\/strong> notebooks\/training for sensitive data.<\/li>\n<li>Restrict inbound access:<\/li>\n<li>Use security groups and avoid public endpoints unless required.<\/li>\n<li>Control outbound:<\/li>\n<li>NAT Gateway with strict egress rules, or enterprise proxy.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Secrets handling<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Do not store secrets in notebooks or code cells.<\/li>\n<li>Use environment variables only for short-lived labs.<\/li>\n<li>For production, use:<\/li>\n<li>RAM roles \/ STS tokens<\/li>\n<li>dedicated secrets management patterns (verify which Alibaba Cloud secrets service your org uses; PAI module integration varies)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Audit\/logging<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use <strong>ActionTrail<\/strong> to audit who created\/changed PAI resources.<\/li>\n<li>Enable OSS access logs or equivalent monitoring where required.<\/li>\n<li>Define log retention aligned to compliance needs.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Compliance considerations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data residency: choose region according to compliance requirements.<\/li>\n<li>PII: enforce data minimization and access controls.<\/li>\n<li>Model risk: implement approval gates for production models.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Common security mistakes<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Public OSS buckets or overly broad bucket policies<\/li>\n<li>Long-lived AccessKeys stored in notebooks<\/li>\n<li>Notebooks left publicly reachable<\/li>\n<li>No separation between dev and prod data<\/li>\n<li>Over-permissive RAM policies (\u201c<em>:<\/em>\u201d style permissions)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Secure deployment recommendations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use private VPC and restrict egress.<\/li>\n<li>Use least privilege for OSS prefixes.<\/li>\n<li>Implement mandatory tagging and periodic permission audits.<\/li>\n<li>Separate roles for training vs deployment.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">13. Limitations and Gotchas<\/h2>\n\n\n\n<blockquote>\n<p>The exact limits vary by region, edition, and module. Always check the quota pages and module docs.<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\">Common limitations<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Region variability<\/strong>: not all PAI modules\/features are available in every region.<\/li>\n<li><strong>GPU constraints<\/strong>: GPU stock and quotas can limit scheduling.<\/li>\n<li><strong>Runtime differences<\/strong>: prebuilt images may have different library versions; pin dependencies.<\/li>\n<li><strong>Network restrictions<\/strong>: private VPC setups often break <code>pip install<\/code> unless egress is designed.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Quotas<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Max concurrent notebooks\/jobs<\/li>\n<li>Max CPU\/GPU quota per account\/region<\/li>\n<li>Max storage or artifact limits (module-specific)<\/li>\n<li>API rate limits<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Regional constraints<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Certain instance families (especially GPU) may exist only in selected regions.<\/li>\n<li>Cross-region data access increases latency and can add cost.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Pricing surprises<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Idle notebooks left running<\/li>\n<li>NAT Gateway and EIP bandwidth charges<\/li>\n<li>OSS request costs for workloads that generate many small reads\/writes<\/li>\n<li>Artifact bloat (multiple checkpoints\/versions)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Compatibility issues<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Model formats supported by serving modules can differ by module\/version.<\/li>\n<li>Some enterprise network patterns (custom DNS\/proxies) require extra setup.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Operational gotchas<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u201cWorks in notebook\u201d \u2260 reproducible training job:<\/li>\n<li>Ensure your training can run non-interactively.<\/li>\n<li>Lack of versioning discipline leads to \u201cunknown model provenance\u201d.<\/li>\n<li>Permission issues often show up as OSS access failures during job runtime.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Migration challenges<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Moving from self-managed to PAI:<\/li>\n<li>requires mapping IAM, artifact storage, and runtime images<\/li>\n<li>Moving away from PAI:<\/li>\n<li>ensure your workflows are defined as code and artifacts stored in portable formats<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Vendor-specific nuances<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Alibaba Cloud RAM policies and OSS permissions are powerful but easy to misconfigure.<\/li>\n<li>Networking patterns (VPC\/NAT\/private endpoints) should be validated early in the project.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">14. Comparison with Alternatives<\/h2>\n\n\n\n<p>Platform For AI (PAI) is one option among managed ML platforms and self-managed stacks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Quick comparison table<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Option<\/th>\n<th>Best For<\/th>\n<th>Strengths<\/th>\n<th>Weaknesses<\/th>\n<th>When to Choose<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Alibaba Cloud Platform For AI (PAI)<\/strong><\/td>\n<td>Teams building ML on Alibaba Cloud<\/td>\n<td>Integrated notebooks + training + (optional) serving; Alibaba Cloud IAM\/VPC integration<\/td>\n<td>Feature availability varies by region\/module; portability depends on your design<\/td>\n<td>You are on Alibaba Cloud and want a managed ML platform<\/td>\n<\/tr>\n<tr>\n<td>Alibaba Cloud self-managed on <strong>ACK (Kubernetes)<\/strong> + Kubeflow\/MLflow<\/td>\n<td>Platform teams needing full control<\/td>\n<td>Maximum customization; portable patterns<\/td>\n<td>Higher ops burden; requires strong platform engineering<\/td>\n<td>You need custom orchestration, multi-cloud portability, or specialized runtimes<\/td>\n<\/tr>\n<tr>\n<td><strong>AWS SageMaker<\/strong><\/td>\n<td>AWS-centric organizations<\/td>\n<td>Mature managed ML suite; broad ecosystem<\/td>\n<td>AWS lock-in; different IAM\/networking model<\/td>\n<td>You run mostly on AWS and want a managed platform<\/td>\n<\/tr>\n<tr>\n<td><strong>Google Vertex AI<\/strong><\/td>\n<td>GCP-centric organizations<\/td>\n<td>Strong managed training\/pipelines; GCP integrations<\/td>\n<td>GCP lock-in; cost model differs<\/td>\n<td>You run mostly on GCP<\/td>\n<\/tr>\n<tr>\n<td><strong>Azure Machine Learning<\/strong><\/td>\n<td>Azure-centric organizations<\/td>\n<td>Enterprise integrations; MLOps tooling<\/td>\n<td>Azure lock-in; learning curve<\/td>\n<td>You run mostly on Azure<\/td>\n<\/tr>\n<tr>\n<td>Self-managed VMs + scripts<\/td>\n<td>Very small teams\/prototypes<\/td>\n<td>Lowest complexity to start<\/td>\n<td>Poor governance; hard to scale; hard to reproduce<\/td>\n<td>Quick prototypes where compliance and scale aren\u2019t required<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Nearest services in the same cloud (Alibaba Cloud)<\/h3>\n\n\n\n<p>Within Alibaba Cloud, the closest \u201calternatives\u201d are often:\n&#8211; Building on <strong>ECS<\/strong> directly (manual notebooks, manual training)\n&#8211; Building on <strong>ACK<\/strong> with open-source ML tooling\n&#8211; Using specific PAI sub-modules directly if your use case is narrower (for example only notebooks or only training)<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">15. Real-World Example<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Enterprise example: Regulated batch scoring with private networking<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A financial services company needs weekly retraining and daily batch scoring for fraud detection. Data is sensitive and must not traverse public internet.<\/li>\n<li><strong>Proposed architecture<\/strong><\/li>\n<li>OSS bucket for curated datasets and model artifacts (encrypted, private)<\/li>\n<li>PAI workspace per environment (dev\/stage\/prod)<\/li>\n<li>Notebook for experimentation in dev workspace<\/li>\n<li>Pipeline for scheduled retraining and evaluation<\/li>\n<li>Training jobs in a VPC-private subnet<\/li>\n<li>Batch scoring job writes results back to OSS for downstream systems<\/li>\n<li>ActionTrail enabled for auditing, strict RAM policies<\/li>\n<li><strong>Why Platform For AI (PAI)<\/strong><\/li>\n<li>Provides managed ML building blocks with Alibaba Cloud IAM\/VPC integration<\/li>\n<li>Elastic compute helps control costs for weekly retraining<\/li>\n<li><strong>Expected outcomes<\/strong><\/li>\n<li>Faster model iteration with governance<\/li>\n<li>Reduced risk through least privilege + private networking<\/li>\n<li>More reproducible retraining and artifact traceability<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Startup\/small-team example: Rapid prototyping and simple artifact management<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem<\/strong>: A small e-commerce startup wants to prototype a recommendation-related model using OSS-stored event data, without hiring platform engineers.<\/li>\n<li><strong>Proposed architecture<\/strong><\/li>\n<li>Single PAI workspace for the team<\/li>\n<li>Managed notebook for feature exploration and training<\/li>\n<li>OSS used as the single source of truth for datasets and model files<\/li>\n<li>Lightweight evaluation scripts; manual promotion of best model to a \u201cproduction\u201d OSS prefix<\/li>\n<li><strong>Why Platform For AI (PAI)<\/strong><\/li>\n<li>Fast start with managed notebook<\/li>\n<li>No need to operate Kubernetes for initial ML work<\/li>\n<li><strong>Expected outcomes<\/strong><\/li>\n<li>Working prototype in days<\/li>\n<li>Clear artifact storage and repeatable runs<\/li>\n<li>Controlled spend by using small instances and shutting down resources<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">16. FAQ<\/h2>\n\n\n\n<p>1) <strong>Is Platform For AI (PAI) a single service or multiple products?<\/strong><br\/>\nPAI is best understood as a <strong>suite<\/strong>. In the console you\u2019ll often see multiple modules (for example notebooks, training, workflows, and serving). The exact modules available depend on region and account\u2014verify in the PAI console.<\/p>\n\n\n\n<p>2) <strong>Do I need OSS to use PAI?<\/strong><br\/>\nYou can run code without OSS, but OSS is strongly recommended for durable datasets and model artifacts. Without OSS, you risk losing artifacts when compute is stopped or re-created.<\/p>\n\n\n\n<p>3) <strong>Is PAI regional or global?<\/strong><br\/>\nIn practice, PAI resources are typically created <strong>per region<\/strong>. Keep your OSS bucket and compute in the same region for best performance and cost.<\/p>\n\n\n\n<p>4) <strong>Can I run PAI entirely inside a VPC?<\/strong><br\/>\nMany PAI modules support VPC networking patterns, but the exact setup depends on the module and region. Verify VPC attachment options in your notebook\/training configuration.<\/p>\n\n\n\n<p>5) <strong>How do I control who can access datasets and models?<\/strong><br\/>\nUse <strong>RAM policies<\/strong> and OSS bucket policies scoped to prefixes. Prefer workspace separation and least privilege.<\/p>\n\n\n\n<p>6) <strong>Do I need GPUs to use PAI?<\/strong><br\/>\nNo. Many ML tasks run well on CPU. GPUs are primarily useful for deep learning training or high-throughput inference.<\/p>\n\n\n\n<p>7) <strong>What ML frameworks are supported?<\/strong><br\/>\nSupport depends on the module (notebook image, training runtime, serving runtime). Always check the module\u2019s \u201csupported frameworks\/versions\u201d doc page for your region.<\/p>\n\n\n\n<p>8) <strong>How do I make notebook experiments reproducible?<\/strong><br\/>\nPin dependencies (<code>requirements.txt<\/code>), store training code in a repo, store datasets and artifacts in OSS with versioned paths, and record environment details (image\/version).<\/p>\n\n\n\n<p>9) <strong>How do I avoid unexpected charges?<\/strong><br\/>\nStop notebook instances when not in use, set auto-stop if available, monitor billing, and control artifact growth in OSS.<\/p>\n\n\n\n<p>10) <strong>Can I schedule retraining pipelines?<\/strong><br\/>\nPAI workflow\/pipeline capabilities commonly support scheduling patterns, but details vary. If not available in your module, use external schedulers (for example CI\/CD or cloud scheduler services) to trigger jobs\u2014verify best practice in your org.<\/p>\n\n\n\n<p>11) <strong>How do I debug training failures?<\/strong><br\/>\nCheck job logs, confirm OSS permissions, validate network egress for dependency downloads, and confirm quota availability.<\/p>\n\n\n\n<p>12) <strong>How do I promote a model to production?<\/strong><br\/>\nUse versioned artifacts and an approval step. A simple pattern is to copy an artifact from <code>...\/staging\/...<\/code> to <code>...\/prod\/...<\/code> in OSS after passing evaluation gates, then redeploy\/consume it.<\/p>\n\n\n\n<p>13) <strong>Does PAI provide a model registry?<\/strong><br\/>\nSome platforms provide registry-like features; availability varies. If your PAI edition\/module lacks a registry, implement a lightweight registry using OSS + metadata in a database or a Git-based release process.<\/p>\n\n\n\n<p>14) <strong>Can I integrate PAI with CI\/CD?<\/strong><br\/>\nYes, typically by triggering training scripts\/jobs and storing outputs in OSS. Exact APIs and automation methods depend on the module\u2014verify PAI API\/SDK docs.<\/p>\n\n\n\n<p>15) <strong>What\u2019s the simplest production-ready pattern with PAI?<\/strong><br\/>\nA good baseline is: versioned datasets + training code in Git, training jobs that produce versioned artifacts in OSS, automated evaluation gates, and controlled deployment\/batch scoring using the approved artifact.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">17. Top Online Resources to Learn Platform For AI (PAI)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Resource Type<\/th>\n<th>Name<\/th>\n<th>Why It Is Useful<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Official documentation<\/td>\n<td>PAI Documentation (Alibaba Cloud Help Center) \u2014 https:\/\/www.alibabacloud.com\/help\/en\/pai\/<\/td>\n<td>Canonical docs for modules, concepts, and workflows<\/td>\n<\/tr>\n<tr>\n<td>Official product page<\/td>\n<td>Alibaba Cloud Platform For AI (PAI) product page \u2014 https:\/\/www.alibabacloud.com\/product\/machine-learning<\/td>\n<td>High-level overview and entry points (verify current page mapping to PAI)<\/td>\n<\/tr>\n<tr>\n<td>Official pricing<\/td>\n<td>Alibaba Cloud Pricing \u2014 https:\/\/www.alibabacloud.com\/pricing<\/td>\n<td>Starting point to find pricing dimensions by region\/product<\/td>\n<\/tr>\n<tr>\n<td>Official OSS pricing<\/td>\n<td>OSS Pricing \u2014 https:\/\/www.alibabacloud.com\/product\/oss#pricing<\/td>\n<td>OSS is a frequent cost driver for ML artifacts and datasets<\/td>\n<\/tr>\n<tr>\n<td>CLI docs<\/td>\n<td>Alibaba Cloud CLI \u2014 https:\/\/www.alibabacloud.com\/help\/en\/cli<\/td>\n<td>Helpful for automation and repeatable operations<\/td>\n<\/tr>\n<tr>\n<td>OSS developer guide<\/td>\n<td>OSS Developer Reference \u2014 https:\/\/www.alibabacloud.com\/help\/en\/oss\/<\/td>\n<td>Upload\/download patterns, SDKs, ossutil usage<\/td>\n<\/tr>\n<tr>\n<td>Audit\/governance<\/td>\n<td>ActionTrail docs \u2014 https:\/\/www.alibabacloud.com\/help\/en\/actiontrail\/<\/td>\n<td>Auditing changes and access patterns for compliance<\/td>\n<\/tr>\n<tr>\n<td>Architecture references<\/td>\n<td>Alibaba Cloud Architecture Center \u2014 https:\/\/www.alibabacloud.com\/architecture<\/td>\n<td>Reference architectures (search for AI\/ML patterns; availability varies)<\/td>\n<\/tr>\n<tr>\n<td>Videos\/webinars<\/td>\n<td>Alibaba Cloud YouTube \u2014 https:\/\/www.youtube.com\/@AlibabaCloud<\/td>\n<td>Talks and demos; search within channel for \u201cPAI\u201d<\/td>\n<\/tr>\n<tr>\n<td>Samples (verify)<\/td>\n<td>Alibaba Cloud GitHub org \u2014 https:\/\/github.com\/aliyun<\/td>\n<td>Some samples may exist; validate repo relevance and maintenance before use<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">18. Training and Certification Providers<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Institute<\/th>\n<th>Suitable Audience<\/th>\n<th>Likely Learning Focus<\/th>\n<th>Mode<\/th>\n<th>Website URL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DevOpsSchool.com<\/td>\n<td>Engineers, DevOps, platform teams, ML engineers<\/td>\n<td>Cloud operations + DevOps adjacent skills; may include MLOps\/PAI-adjacent workflows (verify course catalog)<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.devopsschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>ScmGalaxy.com<\/td>\n<td>Beginners to intermediate DevOps learners<\/td>\n<td>SCM + DevOps fundamentals; useful prerequisites for MLOps practices<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.scmgalaxy.com\/<\/td>\n<\/tr>\n<tr>\n<td>CLoudOpsNow.in<\/td>\n<td>Cloud engineers, SREs, operations teams<\/td>\n<td>Cloud operations practices, monitoring, cost awareness<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.cloudopsnow.in\/<\/td>\n<\/tr>\n<tr>\n<td>SreSchool.com<\/td>\n<td>SREs, reliability engineers, platform teams<\/td>\n<td>Reliability engineering practices applicable to ML platforms<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.sreschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>AiOpsSchool.com<\/td>\n<td>Ops + AI practitioners<\/td>\n<td>AIOps concepts; operational analytics that can complement ML platform operations<\/td>\n<td>Check website<\/td>\n<td>https:\/\/www.aiopsschool.com\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">19. Top Trainers<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Platform\/Site<\/th>\n<th>Likely Specialization<\/th>\n<th>Suitable Audience<\/th>\n<th>Website URL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>RajeshKumar.xyz<\/td>\n<td>Technical training content (verify specific Alibaba Cloud\/PAI coverage)<\/td>\n<td>Learners seeking instructor-led or guided material<\/td>\n<td>https:\/\/rajeshkumar.xyz\/<\/td>\n<\/tr>\n<tr>\n<td>devopstrainer.in<\/td>\n<td>DevOps training (may support MLOps foundations)<\/td>\n<td>DevOps engineers moving toward ML operations<\/td>\n<td>https:\/\/www.devopstrainer.in\/<\/td>\n<\/tr>\n<tr>\n<td>devopsfreelancer.com<\/td>\n<td>Freelance DevOps\/platform expertise<\/td>\n<td>Teams needing short-term coaching or implementation help<\/td>\n<td>https:\/\/www.devopsfreelancer.com\/<\/td>\n<\/tr>\n<tr>\n<td>devopssupport.in<\/td>\n<td>DevOps support\/training resources<\/td>\n<td>Ops teams needing practical troubleshooting and support patterns<\/td>\n<td>https:\/\/www.devopssupport.in\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">20. Top Consulting Companies<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>Company Name<\/th>\n<th>Likely Service Area<\/th>\n<th>Where They May Help<\/th>\n<th>Consulting Use Case Examples<\/th>\n<th>Website URL<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>cotocus.com<\/td>\n<td>Cloud\/DevOps\/engineering services (verify exact offerings)<\/td>\n<td>Platform setup, automation, cloud architecture<\/td>\n<td>PAI workspace setup, OSS governance patterns, CI\/CD integration for training jobs<\/td>\n<td>https:\/\/www.cotocus.com\/<\/td>\n<\/tr>\n<tr>\n<td>DevOpsSchool.com<\/td>\n<td>Training + consulting (verify scope)<\/td>\n<td>DevOps practices, automation, operational readiness<\/td>\n<td>Designing operational controls for ML workloads, cost governance, IaC patterns around cloud resources<\/td>\n<td>https:\/\/www.devopsschool.com\/<\/td>\n<\/tr>\n<tr>\n<td>DEVOPSCONSULTING.IN<\/td>\n<td>DevOps consulting (verify exact offerings)<\/td>\n<td>Delivery pipelines, cloud operations, reliability<\/td>\n<td>Setting up secure VPC patterns for ML compute, monitoring\/alerting strategy for training workloads<\/td>\n<td>https:\/\/www.devopsconsulting.in\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">21. Career and Learning Roadmap<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What to learn before Platform For AI (PAI)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Python fundamentals<\/strong> (data handling, packaging, virtual environments)<\/li>\n<li><strong>ML basics<\/strong>: train\/test split, metrics, overfitting, feature engineering<\/li>\n<li><strong>Cloud basics on Alibaba Cloud<\/strong>:<\/li>\n<li>RAM users\/roles and policies<\/li>\n<li>OSS buckets, prefixes, and permissions<\/li>\n<li>VPC fundamentals (subnets, security groups, NAT)<\/li>\n<li><strong>Data formats<\/strong>: CSV\/Parquet, dataset partitioning, basic ETL concepts<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">What to learn after Platform For AI (PAI)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>MLOps<\/strong> patterns:<\/li>\n<li>pipelines-as-code<\/li>\n<li>artifact versioning strategies<\/li>\n<li>approval gates and model promotion<\/li>\n<li><strong>Observability<\/strong>:<\/li>\n<li>structured logging, metrics, alerting<\/li>\n<li><strong>Security hardening<\/strong>:<\/li>\n<li>private networking, secrets handling, audit trails<\/li>\n<li><strong>Advanced scaling<\/strong>:<\/li>\n<li>distributed training concepts<\/li>\n<li>GPU performance tuning<\/li>\n<li><strong>Serving<\/strong> (if using PAI serving modules):<\/li>\n<li>latency budgeting, autoscaling, canary releases, rollback strategies<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Job roles that use it<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data Scientist<\/li>\n<li>Machine Learning Engineer<\/li>\n<li>Cloud Engineer (AI platform)<\/li>\n<li>DevOps Engineer \/ SRE supporting ML platforms<\/li>\n<li>Security Engineer (governance and access control)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Certification path (if available)<\/h3>\n\n\n\n<p>Alibaba Cloud certification offerings change over time. If you want a formal path:\n&#8211; Check Alibaba Cloud certification program pages and search for AI\/ML tracks (verify current availability): https:\/\/edu.alibabacloud.com\/ (verify)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Project ideas for practice<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Build a repeatable training notebook that always outputs a versioned model to OSS.<\/li>\n<li>Create a pipeline that runs preprocessing + training + evaluation with a pass\/fail gate.<\/li>\n<li>Implement a cost-control checklist (auto-stop, quotas, artifact cleanup).<\/li>\n<li>Implement a secure VPC-only notebook environment and document how package installs work (mirror\/proxy).<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">22. Glossary<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>PAI (Platform For AI)<\/strong>: Alibaba Cloud\u2019s AI &amp; Machine Learning platform suite.<\/li>\n<li><strong>Workspace\/Project<\/strong>: A logical container for organizing ML resources, permissions, and jobs.<\/li>\n<li><strong>PAI-DSW<\/strong>: Common name for PAI\u2019s managed notebook environment (verify module naming in your region).<\/li>\n<li><strong>PAI-Designer<\/strong>: Visual workflow\/pipeline authoring tool (verify availability).<\/li>\n<li><strong>PAI-DLC<\/strong>: Training module based on containerized deep learning workloads (verify availability).<\/li>\n<li><strong>PAI-EAS<\/strong>: Elastic Algorithm Service for model deployment\/serving (verify availability and supported runtimes).<\/li>\n<li><strong>RAM<\/strong>: Resource Access Management\u2014Alibaba Cloud IAM for users\/roles\/policies.<\/li>\n<li><strong>OSS<\/strong>: Object Storage Service\u2014used for datasets and ML artifacts.<\/li>\n<li><strong>VPC<\/strong>: Virtual Private Cloud\u2014private network boundary for compute and data services.<\/li>\n<li><strong>NAT Gateway<\/strong>: Provides controlled outbound internet access for private subnets.<\/li>\n<li><strong>Artifact<\/strong>: Output of ML workflows\u2014models, metrics, logs, checkpoints.<\/li>\n<li><strong>Checkpoint<\/strong>: Intermediate saved state during training, often large and frequent for deep learning.<\/li>\n<li><strong>Least privilege<\/strong>: Security principle of granting only the permissions needed to do a task.<\/li>\n<li><strong>Egress<\/strong>: Outbound network traffic from your VPC\/compute to the internet or other networks.<\/li>\n<li><strong>Batch scoring<\/strong>: Offline prediction across a dataset (as opposed to online inference).<\/li>\n<li><strong>Online inference<\/strong>: Serving predictions via an endpoint for real-time use cases.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">23. Summary<\/h2>\n\n\n\n<p>Alibaba Cloud <strong>Platform For AI (PAI)<\/strong> is a managed <strong>AI &amp; Machine Learning<\/strong> platform suite that helps teams develop models in notebooks, run scalable training jobs, organize workflows, and (optionally) deploy models for inference\u2014while integrating with Alibaba Cloud foundations like <strong>RAM<\/strong>, <strong>VPC<\/strong>, and <strong>OSS<\/strong>.<\/p>\n\n\n\n<p>Key points to carry forward:\n&#8211; <strong>Cost<\/strong> is driven mainly by compute runtime (especially GPU), idle notebooks, and OSS artifact growth\u2014use auto-stop and disciplined artifact lifecycle management.\n&#8211; <strong>Security<\/strong> depends on strong RAM policies, private OSS buckets\/prefixes, and VPC-based isolation for sensitive workloads.\n&#8211; <strong>Fit<\/strong>: Choose PAI when you want a managed ML platform on Alibaba Cloud; reconsider if you need full custom control or you\u2019re heavily invested in another ecosystem.<\/p>\n\n\n\n<p>Next step: read the module-specific docs for the PAI components you will actually use (notebooks vs training vs serving) and extend this lab into a reproducible pipeline that stores versioned artifacts in OSS and enforces evaluation gates before promotion.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI &#038; Machine Learning<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,2],"tags":[],"class_list":["post-9","post","type-post","status-publish","format-standard","hentry","category-ai-machine-learning","category-alibaba-cloud"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts\/9","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":0,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/posts\/9\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/media?parent=9"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/categories?post=9"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/tutorials\/wp-json\/wp\/v2\/tags?post=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}