AJDEVIN AI & TECHNOLOGY SOLUTIONS

Enterprise learning · 20 tracks · 1 year access

Build and operate
enterprise AI.

A structured, end-to-end learning path from the foundations of modern AI to secure, production-ready enterprise systems.

Explore the curriculum
20Tracks
82Modules
218Lesson entries

THE PROGRAM

From AI foundations
to enterprise delivery.

The AJDEVIN Enterprise AI Academy is a comprehensive engineering curriculum for designing, building, evaluating, securing and operating modern AI applications. The sequence connects mathematical and model foundations to retrieval, agents, governance and production deployment.

Learning outcomes

  • Understand Transformer and LLM fundamentals, fine-tuning and multimodal systems.
  • Build grounded RAG applications, agent workflows and enterprise tool integrations.
  • Evaluate, secure, observe and govern AI systems for reliable production use.
  • Package and deliver AI services using Docker, CI/CD and Azure deployment practices.

TECHNOLOGY COVERAGE

LLMs & TransformersRAG & LangGraph Agentic AIMCP & A2A LoRA & QLoRAMultimodal AI Evaluation & LLMOpsAI security GPU optimizationDocker & CI/CD AzureEnterprise governance

HIGH-LEVEL CURRICULUM

Twenty tracks.
One connected journey.

Track and module names are shown as a curriculum preview. Lesson materials, infographics, PDFs, quizzes, code examples and assignments are not available on this public page.

01 Mathematics Behind AI & LLMs Build the mathematical intuition behind modern machine learning, neural networks, embeddings and language models. 4 modules
  1. Linear algebra for AI
  2. Probability and information theory
  3. Calculus and optimization
  4. From mathematics to embeddings
02 LLM Engineering & Transformer Architecture Understand how text becomes tokens, how Transformers contextualize tokens and how autoregressive models generate responses. 4 modules
  1. Text representation
  2. Transformer internals
  3. Training and inference
  4. Model engineering
03 Advanced RAG Engineering Design grounded question-answering systems using ingestion pipelines, vector and lexical retrieval, reranking and evaluation. 4 modules
  1. Knowledge preparation
  2. Retrieval strategies
  3. Grounded generation
  4. RAG evaluation and scale
04 Agentic AI & Multi-Agent Systems Engineer bounded agents that plan, select tools, observe outcomes and recover safely in multi-step workflows. 4 modules
  1. Agent foundations
  2. Reasoning and planning
  3. Multi-agent orchestration
  4. Reliability and safety
05 LLM Fine-Tuning: LoRA, QLoRA & Evaluation Adapt pretrained models with instruction datasets, parameter-efficient methods, quantization and disciplined evaluation. 4 modules
  1. Fine-tuning foundations
  2. LoRA mechanics
  3. QLoRA and quantization
  4. Evaluation and release
06 MCP & Enterprise AI Integration Connect AI applications to enterprise systems through governed tools, resources and standardized context exchange. 4 modules
  1. MCP architecture
  2. Server engineering
  3. Enterprise connectivity
  4. Production governance
07 Production AI, LLMOps & MLOps Move AI applications from notebooks to observable, secure and repeatable production services. 4 modules
  1. Application packaging
  2. Delivery pipelines
  3. Observability
  4. Operations
08 Enterprise AI Architecture & Governance Design enterprise AI platforms that balance business value, security, data governance, reliability and total cost. 4 modules
  1. Architecture foundations
  2. Data and security
  3. Platform architecture
  4. Governance and economics
09 Multimodal AI & Document Intelligence Build systems that interpret text, tables, images, scanned documents and other modalities while preserving structure and provenance. 4 modules
  1. Document understanding
  2. Multimodal representations
  3. Pipeline engineering
  4. Evaluation and deployment
10 GPU Optimization & Sustainable AI Understand accelerator memory, inference bottlenecks, batching, quantization and energy-aware optimization. 4 modules
  1. GPU fundamentals
  2. Inference optimization
  3. Performance engineering
  4. Sustainable AI
11 Generative AI Application Engineering Design complete GenAI applications using model APIs, structured outputs, streaming, context management, caching and resilient application patterns. 4 modules
  1. GenAI application foundations
  2. Application patterns
  3. Reliability and cost
  4. Build and release
12 Prompt Engineering & Context Engineering Move beyond clever prompts into systematic context design, instruction hierarchy, retrieval context, examples and measurable prompt evaluation. 4 modules
  1. Prompt design
  2. Context engineering
  3. Advanced patterns
  4. Evaluation
13 Multimodal Generative AI: Vision, Audio & Video Build applications that reason across text, images, audio and video, with modality-aware preprocessing, grounding and evaluation. 4 modules
  1. Multimodal foundations
  2. Vision applications
  3. Audio and video
  4. Multimodal systems
14 AJDEVIN Agent Hub: Registry, Runtime & Governance Design a governed hub where agents can be registered, discovered, evaluated, permissioned, invoked and monitored as reusable enterprise capabilities. 4 modules
  1. Agent Hub foundations
  2. Discovery and invocation
  3. Governance and trust
  4. Runtime and observability
15 Advanced Multi-Agent Orchestration & A2A Patterns Engineer collaborative agent systems with supervisors, specialist roles, message contracts, handoffs, shared state and failure recovery. 4 modules
  1. Coordination patterns
  2. Communication contracts
  3. State and concurrency
  4. Reliability
16 GenAI Security, Red Teaming & Evaluation Secure GenAI systems against prompt injection, data leakage, unsafe tool calls and unreliable outputs using layered controls and systematic evaluation. 4 modules
  1. Threat landscape
  2. Evaluation engineering
  3. Defence in depth
  4. Red teaming and governance
17 GenAIOps: Model Lifecycle, Evaluation & Observability Operate GenAI applications through prompt and model versioning, evaluation pipelines, telemetry, cost controls and continuous improvement. 4 modules
  1. Lifecycle management
  2. Evaluation operations
  3. Observability
  4. Continuous improvement
18 MCP Engineering & Enterprise Tool Integration Design, secure, test and operate Model Context Protocol servers and clients that expose enterprise capabilities to AI applications. 4 modules
  1. MCP Protocol Foundations and Architecture
  2. Build and Test an MCP Server
  3. MCP Security, Identity and Governance
  4. Deploy and Operate MCP in the Enterprise
19 RAG with LangGraph: From Retrieval to Stateful Agents Build a production-oriented retrieval-augmented generation workflow using LangGraph state, conditional routing, grounded answers and evaluation. 4 modules
  1. RAG Architecture and Knowledge Pipeline
  2. Hybrid Retrieval, Reranking and Grounded Answers
  3. LangGraph Stateful RAG Workflow
  4. RAG Evaluation, Observability and Continuous Improvement
20 Docker, CI/CD and Azure Deployment for RAG + LangGraph Implement a secure delivery pipeline for a LangGraph-based RAG service, from local development through Docker images, automated tests and Azure deployment. 6 modules
  1. Reference Architecture and Repository Design
  2. Build the LangGraph RAG API and Local Test Environment
  3. Dockerize the RAG Application and Run with Compose
  4. Automated CI/CD with GitHub Actions and Container Registry
  5. Deploy to Azure Container Apps Step by Step
  6. Production Operations, Security and Rollback

PAID COURSE · 1 CALENDAR YEAR ACCESS

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