4-Month Master Track — Weekend Batch (Sat & Sun 10:00 AM–12:00 PM IST + Wed Lab)

What you'll learn

1. 1. Week 1: GAIL Module 1 & Modern LLM ArchitectureAI vs ML vs Deep Learning vs Generative AI; why foundation models changed software engineering · How LLMs work: tokens, embeddings, context windows, pre-training, RLHF, multimodal models · Data lifecycle, structured vs unstructured data, labeled vs unlabeled data · ML training paradigms: supervised, unsupervised, reinforcement learning · Hallucinations, latency, token economics, and when NOT to use an LLM
2. 2. Week 2: GAIL Module 2 & Advanced Prompt Engineering + EvalGoogle's foundation model landscape: Gemini (Nano, Flash, Pro, Ultra), Gemma open weights, Imagen, Veo, Chirp, Lyria · Model selection trade-offs: context size, cost, latency, privacy, edge (LiteRT) vs cloud · Zero-shot, few-shot, role prompting, Chain-of-Thought, ReAct, and prompt chaining · Sampling parameters: temperature, top-p, top-k, max output tokens, safety filters, system instructions · Building a 20-case golden evaluation set to score prompts objectively
3. 3. Week 3: Python for AI Engineers, Async APIs & Structured JSONFast-track Python 3.11+ for AI: virtual environments, typing, async/await, httpx, environment variables · Calling Gemini & OpenAI-compatible APIs from Python: multi-turn chat, system prompts, streaming tokens · Guaranteed structured outputs using Pydantic schemas and JSON mode · Production resilience: exponential backoff retries, rate-limit handling, token counters, and cost logging
4. 4. Week 4: GAIL Module 3 (AI Layers & Responsible AI) + MILESTONE 1 DEPLOYED APPThe 5 layers of an enterprise GenAI stack: Infrastructure (TPUs/GPUs), Models, Platform (Agent Platform / Vertex AI), Agents, Applications (Workspace / Gemini Enterprise) · Responsible AI & Google's Secure AI Framework (SAIF): fairness, bias, privacy, explainability, human-in-the-loop · Building & deploying web UIs for AI in Python (Streamlit / Gradio) to a public URL
5. 5. Week 5: GAIL Module 4 (I): Gemini Workspace, Gems & NotebookLM EnterpriseGoogle Workspace with Gemini across Gmail, Docs, Sheets, Slides, Meet, and Vids · Building custom Gems for repeatable team workflows · Gemini Enterprise & Gemini Notebook (NotebookLM Enterprise) for multi-document grounding and synthesis · Agent Platform (Vertex AI) vs Google AI Studio: decision matrix for enterprise architects
6. 6. Week 6: Embeddings, Semantic Search & Vector Databases (Theory + Python)How text & multimodal embeddings work: cosine similarity, dot product, dimensionality, and domain drift · Document ingestion pipeline: PDF/Markdown/HTML parsing, fixed vs recursive vs semantic chunking, overlap & metadata tags · Vector databases in practice: ChromaDB (local/persistent) and Postgres pgvector (Supabase) · When semantic search fails on exact keywords/SKUs and how hybrid search (BM25 + dense vectors) fixes it
7. 7. Week 7: End-to-End RAG Architecture: Citations, Re-Ranking & EvalThe complete RAG loop: Retrieve -> Re-rank -> Augment -> Generate with strict source citations · Google Cloud grounding offerings: Agent Search (Vertex AI Search), RAG APIs, Grounding with Google Search · Preventing hallucinations: strict 'Answer ONLY from context or say I don't know' prompts + citation verification · Evaluating RAG systems: context precision/recall, faithfulness, answer relevance, and prompt-injection defense
8. 8. Week 8: FastAPI + Docker + Cloud Deployment & MILESTONE 2 RAG APPTurning Python AI scripts into production REST APIs with FastAPI: Pydantic request/response models, async streaming endpoints, CORS, API key auth · Containerizing AI backends with Docker (`Dockerfile`, `.dockerignore`, multi-stage builds) · Deploying to Google Cloud Run / Render / Hugging Face Spaces with environment secrets and persistent vector storage
9. 9. Week 9: Customer Engagement Suite, Pre-Built Cloud AI APIs & GAIL MOCK EXAM 1Google Cloud Customer Engagement Suite: Conversational Agents, Agent Assist, Conversational Insights, CCaaS · Pre-built Google Cloud AI APIs: Document AI, Speech-to-Text, Text-to-Speech, Translation, Vision, Video Intelligence, Natural Language · Selecting pre-built APIs vs RAG vs fine-tuning for enterprise RFPs
10. 10. Week 10: GAIL Module 5 (I): Agent Architecture & Tool Calling in PythonWhat makes an AI Agent: reasoning loop (ReAct), memory, planning, and external tools · Deterministic vs generative vs hybrid agents; extensions, functions, data stores, and plugins · Implementing Function Calling / Tool Use in Python: defining JSON tool schemas, executing Python tools safely, and feeding tool outputs back to the model
11. 11. Week 11: Multi-Agent Systems: LangGraph, Google ADK & Model Context Protocol (MCP)Stateful agent workflows and multi-agent orchestration (Router, Researcher, Coder/Analyst, Critic/Verifier) · Building production agent graphs with LangGraph / Google Agent Development Kit (ADK) · Model Context Protocol (MCP): connecting agents to standardized data sources and enterprise tools · Human-in-the-loop approval gates, max-step budgets, and tool-error recovery
12. 12. Week 12: GAIL Module 5 (II): Enterprise Agent Platform, MLOps & MILESTONE 3 AGENTGemini Enterprise Agent Platform (Vertex AI): Model Garden, Model Builder, AutoML, Agent Builder, Agent Search · MLOps for GenAI: Model Registry, versioning, evaluation pipelines, drift & performance monitoring, Feature Store · Fine-tuning options (supervised tuning, RLHF, distillation, LoRA) vs RAG: cost and maintenance trade-offs
13. 13. Week 13: Business Strategy, ROI Modelling, SAIF Security & GAIL MOCK EXAM 2Executive GenAI selection framework: scale, customization, latency, privacy, connectivity, and build-vs-buy TCO · Top-down + bottom-up transformation strategy, change management, and KPI/ROI calculation · Secure AI in depth: Google SAIF, IAM least privilege, VPC Service Controls, Security Command Center, data residency & DLP
14. 14. Week 14: Dual Capstone Build Sprint: Architecture, Code & EvaluationScoping a high-impact Dual Capstone that satisfies both hiring managers and enterprise architecture panels · End-to-end engineering sprint: combining FastAPI, Vector DB/RAG, Multi-Agent tools, UI polish, and automated eval suite · Writing the 6-page Executive GenAI Proposal (business case, Google Cloud architecture diagram, model/RAG choices, SAIF security, and 12-month ROI)
15. 15. Week 15: AI Career Accelerator (Resume, GitHub, System Design Interviews) & GAIL Exam BookingStructuring an AI Engineer / GenAI Leader resume and LinkedIn profile around 3 deployed URLs and quantified eval metrics · GitHub portfolio makeover: pinned repositories, architecture diagrams, 2-minute Loom/video demos, clean READMEs · Mock Technical & Leadership Interviews: LLM/RAG system-design scenarios + GAIL situational business questions · Step-by-step official Google Cloud GAIL Exam registration walkthrough (online-proctored vs Pearson VUE test centre)
16. 16. Week 16: Final Exam Cram Clinic & DUAL CAPSTONE DEMO DAYRapid-fire 60-minute GAIL exam cram: high-frequency exam traps, Google Cloud product naming cheatsheet, time-management strategy for 50-60 questions in 90 minutes · Production launch checklist: logging, error boundaries, README, and demo script · Grand Finale Dual Capstone Demo Day in front of instructors and invited industry guests

Final project demo: Dual Master Capstone: Executive Enterprise GenAI Proposal + Deployed Production RAG & Multi-Agent System. Every graduate ships a two-part flagship capstone: (1) A 6-8 page Executive Architecture & Business Proposal mapped to Google Cloud's GAIL framework (use case, KPI/ROI model, Google Cloud solution layer, model trade-offs, grounding design, SAIF security & Responsible AI rollout), AND (2) A live, publicly deployed full-stack RAG & Multi-Agent application in Python (FastAPI + Vector DB + LangGraph/ADK + Docker/Cloud Run) with a clean GitHub repository, architecture diagram, evaluation scorecard, and 2-minute demo video.