AI, GenAI & Agentic AI Training in Hyderabad — Build Real AI Agents, Not Slides

1. AI + Python foundations

• Python for AI (typing, async, packaging)
• NumPy / pandas essentials
• ML intuition — train/test, overfitting, metrics
• Deep learning + transformers overview
• NLP + embeddings intuition
• Compute: GPU vs CPU, when you actually need training

2. LLMs + prompt engineering

• Tokenisation, context windows, cost per token
• System / few-shot / structured-output prompting
• Function (tool) calling + JSON mode
• Prompt evaluation harness + regression tests
• Hallucination, grounding and guardrails

3. Generative AI + RAG

• Embeddings + similarity search
• Vector databases — pgvector, FAISS, Pinecone
• Chunking strategies that don't lose context
• Retrieval + re-ranking + citations
• RAG evaluation (faithfulness, relevance)
• Fine-tuning vs RAG — when each is right

4. Agentic AI — LangChain + LangGraph

• Agent loops, tools, ReAct
• LangGraph state machines + conditional edges
• Memory (short-term, long-term, summaries)
• Multi-agent systems + orchestration
• Human-in-the-loop approvals
• Agent evaluation + failure handling

5. MCP (Model Context Protocol)

• Why MCP exists — standard tool interface
• Build an MCP server (tools + resources)
• Connect agents to Jira, GitHub, ServiceNow, AWS
• Auth, scoping and safety for tool access

6. Enterprise AI on AWS + Azure

• AWS Bedrock — models, Knowledge Bases, Agents, AgentCore
• Azure OpenAI + Azure AI Search (enterprise RAG)
• Private data, PII handling, prompt-injection defence
• Cost, quota and model-routing strategy
• AI governance + responsible-AI basics

7. Deploy + operate AI in production

• Containerise with Docker
• Deploy on Kubernetes (EKS/AKS) or serverless
• CI/CD for prompts + agents
• Tracing/observability (LangSmith / OpenTelemetry)
• Latency, caching and token-cost control
• Secrets, rate-limits and guardrails in prod

8. Capstone agent projects (you build + deploy)

• Jira AI agent (triage + comment + transition issues)
• GitHub agent (review + summarise PRs)
• RAG assistant over your own docs
• Multi-agent research → write → review pipeline
• MCP-connected AWS cloud agent
• Deploy one capstone live on AWS

Do I need a maths or ML PhD background to learn AI and Agentic AI here?

No. This is an engineering-first AI course. We start from Python + practical ML intuition, then go deep on LLMs, RAG and agents — the skills recruiters actually hire for in 2026. You build and deploy real AI agents; you don't spend the course deriving equations. Non-IT switchers get a Python + fundamentals onboarding before the AI core.

What is Agentic AI, and will I actually build agents — not just learn theory?

Agentic AI is about LLMs that take actions through tools, not just answer questions. You build real agents: a Jira agent that triages and transitions issues, a GitHub PR-review agent, a RAG assistant over your own docs, and a multi-agent research→write→review pipeline — using LangChain, LangGraph and MCP, then deploy one capstone live on AWS.

Does the course cover RAG, LangGraph, MCP and enterprise AI on AWS Bedrock / Azure OpenAI?

Yes — all of them. RAG end-to-end (embeddings, vector DBs, re-ranking, evaluation), LangGraph state machines, the Model Context Protocol for connecting agents to real tools, and Enterprise AI on both AWS Bedrock (Knowledge Bases, Agents, AgentCore) and Azure OpenAI + Azure AI Search, with security, cost control and governance.

Is the AI training in Ameerpet, Hyderabad classroom or online, and what does it cost?

Both — classroom batches at the Ameerpet campus (513, 5th Floor, Aditya Enclave, beside Ameerpet metro) and live online batches in parallel with the same instructor, labs and recordings. Flat Rs.30,000 (same for classroom + online), 15% off for group enrolment (3+). The basis for every fee + placement claim is at /placements/methodology/.

How is this different from a free online AI/ChatGPT course on YouTube?

Free content teaches you to call an API. This course teaches you to ship — retrieval that doesn't hallucinate, agents with human-in-the-loop and memory, MCP tool servers, and deployment with Docker/Kubernetes/CI-CD, cost control and observability — reviewed live by a trainer who builds these systems, with real projects for your portfolio and placement support.

Will this prepare me for AI / GenAI job interviews and placements?

Yes. Alongside the build work you get AI/GenAI interview prep, portfolio reviews of your agent projects, and 100% placement assistance — resume + LinkedIn, mock interviews and referrals to our 2,000+ partner companies. Placement itself is not guaranteed; it depends on your interview performance and current market conditions.