Azure Openai Advanced Training
Design resilient, governed, enterprise-scale Azure OpenAI platforms with an AI gateway, multi-region failover, hardened safety and LLMOps. Learn Azure Openai through a clear Foundation to Advanced path, with hands-on labs, live cohort options, certification support and full LMS access.
Last updated
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Azure Openai levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
About Azure Openai Advanced Training.
Understand where Azure Openai fits in modern software delivery, what this level covers, and how the program builds practical confidence step by step.
Program overview
Azure Openai is taught as a practical technical skill for modern engineering, operations and platform teams. The course explains where the tool fits, how it is used in real delivery environments, and how learners can apply it through guided practice.
This DevOpsSchool Azure Openai program is built for working professionals who want usable skills instead of only conceptual exposure. The training emphasizes live examples, workflow clarity, operational controls and progressive skill depth from Foundation to Advanced.
Private Azure Openai training for delivery teams, platform groups and engineering organizations.
DevOpsSchool provides corporate Azure Openai training for teams that need faster adoption, workflow standardization, administrative control, integration guidance and governance-ready rollout. Private batches can be aligned to development practice, release workflow, access model, maturity level and internal tooling.
- Private online or classroom delivery for engineering teams
- Aligned to Azure Openai usage maturity, admin depth and collaboration patterns
- Useful for rollout, migration, governance and workflow consistency goals
- Can be tailored for developers, administrators, DevOps teams and leadership stakeholders
Why teams use DevOpsSchool
DevOpsSchool combines live demonstration, practical workflow discussion, implementation-oriented examples, and strong post-session learning support. That makes the training more usable for real teams than generic slide-led courses.
Choose any level. The page still shows the full progression.
Foundation, Essential, Intermediate and Advanced are presented as one linked training flow. Self-paced buyers get all levels by default, even if they start from only one course page.
Instructor-led, self-paced and corporate delivery options.
Compare delivery format, duration, level access and batch style. Fees are shown only in the top decision panel and once again after the agenda.
| Duration | Mode | Level access | Batch |
|---|---|---|---|
| 8 to 10 Hrs. max | Live & Interactive | Advanced | Public batch |
| 8 to 10 Hrs. max | 1:1 Live training | Advanced | Starts immediately |
| 1 year LMS access | Self-paced video | All 4 levels | Anytime access |
| 5 Days | Corporate | Azure Openai Advanced Training | Private team batch |
What learners should be able to do after Azure Openai training.
The outcomes are written for practical delivery teams: developers, DevOps engineers, release engineers, administrators and technical leads.
Use Azure OpenAI responsibly
Choose models, write effective prompts and apply content filtering and responsible AI policies.
Build generative AI apps
Develop chat, RAG, embedding, image, speech and agent features with Azure OpenAI APIs.
Run AI securely at scale
Secure, monitor, govern and scale Azure OpenAI with private networking and an AI gateway.
Why this skill, what you will achieve, and who should attend.
Use this section to decide whether the course matches your role, current experience and expected Azure Openai responsibilities before reviewing the detailed agenda.
Why Azure Openai matters
- Azure Openai helps teams standardize a critical part of their software delivery, infrastructure, automation or operations workflow.
- It gives learners a repeatable way to move from basic usage to confident execution in team and enterprise environments.
- For DevOps and platform teams, Azure Openai becomes more valuable when connected with automation, governance, monitoring and delivery practices.
- Teams using Azure Openai effectively reduce ad-hoc execution and move toward consistent engineering standards.
Objectives
- Understand Azure Openai concepts, architecture, terminology and operational usage patterns.
- Learn practical configuration, workflow design, access controls, team usage and quality practices.
- Apply Azure Openai in real delivery workflows using automation, integrations, traceability and governance patterns.
- Build practical confidence to use Azure Openai for individual, team and organizational use cases.
Pre-requisites
- Basic computer literacy and familiarity with software development workflows.
- Helpful but not mandatory: introductory DevOps, cloud, automation or software delivery awareness.
- No deep programming background is required for the Foundation and Essential levels.
Who this course is for
- Developers, administrators and engineers who need practical Azure Openai usage skills.
- DevOps engineers, release engineers, SREs and platform teams managing delivery standards.
- Engineering leads and technical managers responsible for workflow quality, access controls and governance.
- Corporate teams migrating to or standardizing on Azure Openai.
Small-batch live training starts every month.
Weekday batches start on the 1st and weekend batches on the first Saturday of every month. One-to-one (1:1) training can start immediately; corporate sessions are scheduled on demand.
Weekend live batch
Starts on the first Saturday of every month. Weekend sessions with demos, labs and Q&A; max 10 learners.
Weekday live batch
Starts on the 1st of every month. Weekday sessions for working professionals and distributed teams; max 10 learners.
Ask advisorAgenda depth up to the selected level.
This page shows the learning flow from Foundation through Advanced. Each level adds to the previous one, and the agenda stops at the current page level. Day counts are for corporate (day-wise) delivery; live online batches cover the same agenda in max 8 to 10 hours.
Foundation Level: generative AI concepts, models, safety and promptingDay 1
- What is generative AI: large language models, tokens, context windows and foundation models
- What is Azure OpenAI: describing the service, its relation to OpenAI and how to use it via portal, SDKs and REST
- Models: GPT-4o, GPT-4.1 and GPT-5 families, o-series reasoning, embeddings, image models and legacy GPT-3, GPT-35-Turbo, DALL-E
- Capabilities: OpenAI natural language, code generation and image-generation capabilities with examples
- Quotas and limits: tokens and requests per minute, regional quotas and model availability by region
- Pricing: input and output tokens, standard versus provisioned throughput and batch processing
- Quick start: create a resource, deploy a chat model and test it in the Azure AI Foundry playground
- Access and responsible AI policies: limited access, code of conduct, transparency note, data, privacy and security
- Abuse monitoring and content filtering: harm categories, severity levels and Prompt Shields
- Embeddings concept: vectors, similarity and search, clustering and recommendation use cases
- Prompt engineering: intro and techniques, zero-shot, few-shot, step-by-step reasoning, delimiters and output formats
- System message design: role, scope, tone, safety instructions and grounding rules
- Red teaming large language models: planning, harm categories and tools such as PyRIT
- Knowledge check and summary: reviewing service concepts, safety controls and prompting skills
Essential Level = Foundation + deployments, SDKs, chat, embeddings and filtersDay 2 · 2 days cumulative
- Resource creation and model deployment: portal and Azure CLI, Global Standard, Data Zone, Standard and Provisioned types
- Chat completions: GPT-35-Turbo and GPT-4 class models, messages and roles, temperature, top_p and max tokens
- API styles: legacy Completions, Chat Completions and the Responses API with api-version choices
- OpenAI vs Azure OpenAI in Python: AzureOpenAI client, endpoints, deployments, API keys and Entra ID tokens
- Configure content filters: custom filter configurations, severity thresholds, blocklists and annotations
- Work with code: code generation, explanation and refactoring with GPT models, successors to the retired Codex models
- Function calling and structured outputs: tool definitions, JSON schema responses and parsing
- Embeddings tutorial: text-embedding-3 models, chunking text and cosine similarity search
- Speech to speech chat tutorial: Azure AI Speech with a GPT model and the gpt-4o realtime API
- Image generation: DALL-E 3 and gpt-image-1 deployments, sizes, quality and edits
- Robust calls: streaming responses, token counting with tiktoken and retries for 429 errors
Intermediate Level = Essential + RAG, fine-tuning, agents, security and operationsDay 3 · 3 days cumulative
- Retrieval-augmented generation: Azure AI Search vector and hybrid search, semantic ranker and citations
- Use with large datasets: chunking strategies, indexing pipelines and the Batch API for bulk jobs
- Prepare dataset and fine-tune your model: JSONL chat format, training and validation files, deploying the fine-tuned model
- Manage identity: Entra ID authentication, managed identities and Cognitive Services OpenAI roles
- Manage quota: TPM allocation per deployment, dynamic quota and provisioned throughput units
- Use virtual networks: private endpoints, disabling public access and trusted service access
- Encryption of data at rest: Microsoft-managed versus customer-managed keys in Key Vault
- Monitor Azure OpenAI: Azure Monitor metrics, diagnostic logs and Application Insights tracing
- Plan and manage costs: cost analysis, budgets, prompt caching and token-efficient prompts
- Evaluations: groundedness, relevance, coherence and safety evaluators in Azure AI Foundry
- Agents: Azure AI Foundry Agent Service with file search, code interpreter and function tools
- App frameworks: Semantic Kernel or LangChain apps hosted on Azure Functions or App Service
- Troubleshooting: 429 throttling, content filter blocks, context length errors and regional model availability
Advanced Level = Intermediate + resilience, AI gateway, governance and LLMOpsDays 4-5 · 5 days cumulative
- Business continuity and disaster recovery: multi-region deployments, Global and Data Zone types and failover design
- AI gateway with API Management: load balancing across deployments, token limit policies, semantic caching and token metrics
- Provisioned throughput at scale: PTU capacity planning, spillover to standard deployments and utilisation tracking
- Enterprise landing zone: hub and spoke networking, private DNS, shared AI services and per-team access
- Safety hardening: jailbreak and indirect prompt injection defences, groundedness detection and Defender for Cloud AI alerts
- Governance: Azure Policy for AI services, approved model lists, data residency and audit logging
- LLMOps: prompt versioning, evaluation gates in CI/CD, model version upgrades and retirement planning
- Multi-agent systems: orchestration with Semantic Kernel or AutoGen and tool integration over MCP
- Advanced customisation: distillation and reinforcement fine-tuning where available, comparing with RAG
- Cost optimisation: model routing, smaller models for simple tasks, batch processing and caching
- Responsible AI programme: impact assessments, transparency notes for your app, human oversight and EU AI Act duties
- Capstone: an enterprise assistant with RAG, agents, AI gateway, private networking, evaluations and DR
Choose live training or a full year of self-paced access.
If someone buys Foundation, Essential, Intermediate or Advanced as self-paced video, the price stays the same and access is granted to all four levels by default.
Self-paced video subscription
Billed yearly at ₹9,996. This includes all four levels, complete LMS access for one year, plus 20+ additional courses and 50+ tools access.
- All four Azure Openai levels in one price.
- 20+ additional courses and 50+ tools included, even when subscribing to one course.
- Best fit for learners who want full-library access and flexible progress.
Live & Interactive session
Instructor-led cohort, max 8 to 10 hours, with demos, labs, Q&A, assignments and practical review.
- Live small-batch cohort from soon.
- Interactive discussion and lab troubleshooting.
- Recommended for teams that need guided implementation.
1:1 Live training
The same agenda, taught one-to-one by the trainer. Start immediately, without waiting for the next batch. Max 8 to 10 hours, one-to-one.
- Start immediately: no waiting for the monthly batch.
- One learner, one trainer: full attention on your questions.
- Sessions planned around your working hours.
- Your pace, with examples from your own projects and tools.
Method of training and assessment.
The course emphasizes practical demonstration, hands-on labs and assignment-based validation instead of passive lecture-only learning.
| SL | Method of training and assessment | Weightage |
|---|---|---|
| 1 | Understanding the problem space | 5% |
| 2 | Concept discussion | 10% |
| 3 | Live demo and walkthrough | 25% |
| 4 | Lab and hands-on exercises | 50% |
| 5 | Assignments and evaluation | 10% |
Labs, exercises and real workflow practice.
Every level moves from explanation to hands-on practice, review and troubleshooting.
Deploy a model and shape it
Create an Azure OpenAI resource, deploy a chat model, and iterate on a system message and few-shot prompt in the playground while observing content filter results.
Build a grounded chat assistant
Write a Python app with the AzureOpenAI client using Entra ID, add function calling and embedding search over a small document set, and apply a custom content filter.
Secure RAG app on private network
Index documents in Azure AI Search, build a RAG endpoint with managed identity and private endpoints, evaluate groundedness and monitor token usage and cost.
Capstone: enterprise AI assistant platform
Front two regional deployments with an API Management AI gateway, run a secured RAG agent behind it, enforce policy and evaluation gates, and test regional failover.
Azure Openai is taught as part of a working delivery ecosystem.
You learn where the tool sits in the wider engineering workflow, not in isolation.
Core foundations
Terminology, concepts, architecture and the operating model behind the tool.
Workflow traceability
Map practical work to tasks, approvals, documentation and delivery visibility.
Automation and integration
Use Azure Openai with validation checks, automation hooks and delivery workflows.
Governance controls
Operate with permissions, approvals, policy checks and production-safe workflows.
How your practical work is assessed.
Your certificate is backed by labs, assignments, capstone thinking and completion evidence.
Assignments
Level-specific assignments verify setup, configuration, workflow quality and operating discipline.
Hands-on validation
Learners are expected to complete practical usage, automation and troubleshooting exercises.
Completion record
Certificate issuance is tied to demonstrated participation, practical completion and course-level coverage.
Led by Rajesh Kumar and the DevOpsSchool practitioner network.
Training is grounded in practical DevOps, SRE, security, release and platform engineering experience with real enterprise delivery context.
Earn a DevOpsSchool Azure Openai certificate.
Completion includes a digital certificate with credential details, practical assignments and a learning record aligned to the selected Azure Openai level.
Certificate verificationCertificate of Completion
Azure Openai Advanced Training
Unique credential ID, verifiable online
What learners say about training with Rajesh Kumar.
“Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.”
“Very well organized training, helped a lot to understand the AWS concept and detailed related to various tools. Very helpful”
“The training was very useful and interactive. Rajesh helped develop the confidence of all.”
Rated 4.6 on Google · All testimonials · Video reviews on YouTube
Quoted as published, from learners in Rajesh Kumar's live trainings across DevOpsSchool courses.
Used by developers, DevOps engineers, release leads and engineering managers.
Private batches are available for teams that want the same agenda aligned to their own tools and workflows.
Why this Azure Openai path is different.
The value is not only one course. The subscription unlocks the full learning ecosystem for one year.
| Feature | DevOpsSchool Azure Openai Training | Typical short course |
|---|---|---|
| Levels | Foundation, Essential, Intermediate, Advanced | Single generic syllabus |
| Self-paced access | All four levels plus 20+ courses and 50+ tools | Usually one course only |
| Live training | Demos, labs, Q&A and assignment review | Often lecture-heavy |
| Certificate | DevOpsSchool completion certificate | Varies by provider |
Questions learners ask before joining.
Does Foundation cost less than Advanced?
No. Self-paced pricing is intentionally the same because access includes all four levels by default.
What is included in the yearly subscription?
One year of complete LMS access, all four levels for the selected course, plus 20+ additional courses and 50+ tools access.
Can corporate teams request customization?
Yes. The live format can be aligned to team tools, governance requirements, workflows and internal delivery standards.
What is the recommended sequence?
Foundation → Essential → Intermediate → Advanced. The agenda is designed so each level builds on the previous one.
Talk to DevOpsSchool before choosing the batch.
Use this support section for batch questions, corporate planning, LMS access clarifications, or guidance on which Azure Openai level fits your team.
India / Worldwide: +91 99057 40781
USA: +1 (469) 756-6329
Email: contact@DevOpsSchool.com
Chat on WhatsAppExtend the course with articles, explainers and workflow guidance.
Continue learning with supporting articles that connect Azure Openai practices to DevOps, automation, governance and delivery workflows.
Training snapshots and certificate preview.
A look at the completion certificate and the course.
Certificate preview
See what course completion evidence looks like.
Tool-focused training
Course visuals reinforce the Azure Openai workflow and subject area.
Live batch experience
Training supports live online, private team and workshop-style delivery.
Start Azure Openai Advanced training.
Next weekday batch starts on the 1st; weekend batch on the first Saturday. Batches are capped at 10 learners. Send your details and DevOpsSchool will share the syllabus and batch options.