{"id":47425,"date":"2024-11-14T19:07:39","date_gmt":"2024-11-14T19:07:39","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=47425"},"modified":"2025-07-12T05:54:53","modified_gmt":"2025-07-12T05:54:53","slug":"learning-roadmap-for-mlops-and-machine-learning","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/learning-roadmap-for-mlops-and-machine-learning\/","title":{"rendered":"Learning Roadmap for MLOps and Machine Learning"},"content":{"rendered":"\n

Below is a structured table of problem areas, each with a primary and secondary tool recommendation to guide your learning in MLOps and Machine Learning. This table will serve as a roadmap, helping you learn and master the essential skills and tools in each area.<\/p>\n\n\n\n

Problem Area<\/strong><\/th>Domain<\/strong><\/th>Most Recommended Tool<\/strong><\/th>Second Recommended Tool<\/strong><\/th>Description \/ Learning Path<\/strong><\/th><\/tr><\/thead>
Foundational Knowledge<\/strong><\/td>MLOps Introduction<\/td>N\/A<\/td>N\/A<\/td>Start with MLOps basics, covering CI\/CD for ML, model lifecycle, and pipeline fundamentals. Resources: Courses, documentation on MLOps concepts from Google, Microsoft, or AWS.<\/td><\/tr>
Environment Setup<\/strong><\/td>Containers<\/td>Docker<\/td>Podman<\/td>Learn Docker basics for containerizing models, deploying environments, and bundling dependencies. Essential for reproducible environments.<\/td><\/tr>
<\/td>Container Orchestration<\/td>Kubernetes<\/td>OpenShift<\/td>Master Kubernetes for managing containerized workloads at scale. Start with basics (pods, deployments), then explore more complex topics (networking, storage).<\/td><\/tr>
Data Management<\/strong><\/td>Workflow Orchestration<\/td>Apache Airflow<\/td>Prefect<\/td>Use Airflow to create data pipelines and schedule ETL workflows, Prefect for simpler, Pythonic workflows. Build basic to complex data processing pipelines.<\/td><\/tr>
<\/td>Feature Engineering & Storage<\/td>Feast (Feature Store)<\/td>Delta Lake<\/td>Feast handles feature storage and serving, especially for real-time ML. Delta Lake helps manage data lineage and data versions.<\/td><\/tr>
Experiment Tracking<\/strong><\/td>Experiment Logging<\/td>MLflow<\/td>Weights & Biases (W&B)<\/td>Start with MLflow for tracking experiment parameters, results, and metadata. W&B offers a richer interface and deeper integrations.<\/td><\/tr>
<\/td>Visualization<\/td>TensorBoard<\/td>Weights & Biases (W&B)<\/td>TensorBoard is ideal for visualizing deep learning training. W&B provides broader visualization across models and datasets.<\/td><\/tr>
Model Versioning<\/strong><\/td>Model Tracking & Registry<\/td>MLflow<\/td>DVC (Data Version Control)<\/td>MLflow handles model versioning and packaging; DVC offers data and model versioning in Git for reproducibility.<\/td><\/tr>
Model Training<\/strong><\/td>Training Environment<\/td>Jupyter Notebooks<\/td>Google Colab<\/td>Use Jupyter for local experiments, Google Colab for cloud-based training with GPU access. Develop familiarity with these interactive environments.<\/td><\/tr>
<\/td>Framework – Classical ML<\/td>scikit-learn<\/td>XGBoost<\/td>Start with scikit-learn for foundational ML algorithms; XGBoost for more complex ensemble models. Great for both experimentation and deployment readiness.<\/td><\/tr>
<\/td>Framework – Deep Learning<\/td>PyTorch<\/td>TensorFlow<\/td>PyTorch for flexible, research-oriented workflows; TensorFlow for large-scale, production-grade models. Learn basics, then progress to advanced training techniques.<\/td><\/tr>
<\/td>Distributed Training<\/td>Horovod<\/td>Distributed TensorFlow<\/td>Horovod integrates with PyTorch and TensorFlow, making distributed training simpler. Useful for handling large datasets and models.<\/td><\/tr>
Model Testing & Validation<\/strong><\/td>Unit Testing<\/td>Pytest<\/td>Unittest<\/td>Pytest is versatile and widely used for writing test cases; Unittest provides a more basic alternative in Python\u2019s standard library.<\/td><\/tr>
<\/td>Data Validation<\/td>Great Expectations<\/td>Pandera<\/td>Great Expectations is a robust tool for data quality checks; Pandera integrates with Pandas for schema and data validation.<\/td><\/tr>
<\/td>Model Testing<\/td>Deepchecks<\/td>alibi-detect<\/td>Deepchecks automates tests for data and model validation, alibi-detect helps detect data and concept drift.<\/td><\/tr>
Model Deployment<\/strong><\/td>Model Serving<\/td>TensorFlow Serving<\/td>TorchServe<\/td>TensorFlow Serving and TorchServe are model-serving frameworks optimized for TensorFlow and PyTorch, respectively. They streamline deployment into production.<\/td><\/tr>
<\/td>API Creation<\/td>FastAPI<\/td>Flask<\/td>FastAPI is ideal for building APIs for model inference; Flask is simpler but also effective for deploying models.<\/td><\/tr>
<\/td>Kubernetes Integration<\/td>Kubernetes<\/td>Knative<\/td>Kubernetes manages containerized deployments; Knative simplifies serverless deployments on Kubernetes.<\/td><\/tr>
Monitoring & Logging<\/strong><\/td>Infrastructure Monitoring<\/td>Prometheus + Grafana<\/td>DataDog<\/td>Prometheus and Grafana are open-source tools for monitoring metrics; DataDog is a more complete observability platform with ML integrations.<\/td><\/tr>
<\/td>Model Monitoring<\/td>Evidently AI<\/td>Fiddler AI<\/td>Evidently AI monitors model drift, performance degradation, and data quality; Fiddler AI adds explainability and additional ML-specific metrics.<\/td><\/tr>
<\/td>Logging<\/td>ELK Stack (Elasticsearch, Logstash, Kibana)<\/td>Fluentd<\/td>ELK Stack is widely used for centralized logging; Fluentd is an alternative for aggregating logs across environments.<\/td><\/tr>
CI\/CD in MLOps<\/strong><\/td>CI\/CD Pipelines<\/td>GitHub Actions<\/td>Jenkins<\/td>GitHub Actions integrates directly with GitHub for CI\/CD; Jenkins is highly customizable for more complex CI\/CD pipelines.<\/td><\/tr>
<\/td>CI\/CD in Data Pipelines<\/td>DVC Pipelines<\/td>Tecton<\/td>DVC Pipelines are Git-integrated for version-controlled ML pipelines; Tecton supports feature pipelines for real-time model deployment.<\/td><\/tr>
<\/td>CI\/CD in Model Pipelines<\/td>Kubeflow Pipelines<\/td>MLflow Pipelines<\/td>Kubeflow Pipelines is Kubernetes-native for end-to-end ML workflows; MLflow Pipelines allows for modular pipeline building in MLflow.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n

Suggested Learning Plan<\/h3>\n\n\n\n
    \n
  1. Start with Foundations<\/strong>: Learn MLOps basics, environment setup with Docker and Kubernetes, and workflow orchestration with Apache Airflow or Prefect.<\/li>\n\n\n\n
  2. Model Experimentation and Tracking<\/strong>: Work with Jupyter Notebooks, MLflow for experiment tracking, and try basic visualizations with TensorBoard.<\/li>\n\n\n\n
  3. Model Training and Testing<\/strong>: Gain experience with PyTorch\/TensorFlow for deep learning and scikit-learn for classical ML. Use Pytest and Great Expectations for testing workflows.<\/li>\n\n\n\n
  4. Model Packaging and Versioning<\/strong>: Use MLflow for tracking and model versioning, and Docker for containerizing models.<\/li>\n\n\n\n
  5. Deployment and Monitoring<\/strong>: Practice deploying models using TensorFlow Serving or FastAPI, and set up monitoring with Prometheus and Grafana.<\/li>\n\n\n\n
  6. Advanced CI\/CD Workflows<\/strong>: Explore CI\/CD with GitHub Actions or Jenkins, and dive into Kubeflow Pipelines for building end-to-end MLOps pipelines.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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