House of Data/Generative AI Flagship
Flagship · 6 months
The full build, foundation to roof.
Six months, starting from the beginning. No assumed coding background, deeper specialisation, production LLMOps, more capstone projects and longer placement support. This is the one to take if you want the whole thing rather than the quickest route through it.
100+ placed in MNCs in 2026
We Train for Careers, Not Just Certificates.
Learn. Build. Get Placed.
Only on the Flagship
Five things the Fast Track does not include.
The extra three months are not more lectures. They are the part where you stop being a student with a certificate and start being someone with a product, an internship on the CV and people who will take your call.
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01
Internship opportunities
Real work on a live product, with a team and a deadline — the line on your resume that answers "but have you done this outside a classroom?"
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02
Your idea, turned into a project
Bring whatever you have: a rough thought on a phone note, a half-built notebook, a problem from your old job. We scope it with you until it is something buildable.
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03
Built end to end, with help
Data ingestion, model, evaluation, API, front end, deployment and monitoring. Not a demo — something that stays up and that you can hand someone a link to.
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04
Introductions to at least 3 investors
If what you have built is worth funding, we put it in front of a minimum of three people who fund things, and help you prepare for that room.
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3 months of job support after you join
The first ninety days in an AI role decide the next three years. You keep your mentor through them — the code reviews, the "what do I say in standup", all of it.
All five sit on top of everything in the Fast Track. Fee ₹99,999 plus the post-placement fee, instalments available.
In the fee
Everything that comes with it.
No tiers, no add-ons, no module you have to buy separately. One fee, and this is what it buys.
100+ days of live training taught in the room in Dilsukhnagar or online, weekday or weekend — every session live, none of it pre-recorded.
Doubt sessions every week a separate slot for questions, plus a Questions tab in your portal where a trainer answers in writing.
Four projects you deploy yourself an end-to-end RAG system, a document extraction pipeline and two real-time builds — on public URLs, not in a notebook.
1:1 mentoring on your profile we write your resume, market it to hiring partners and keep going until you are placed.
A certificate anyone can check numbered and signed, and a recruiter can verify it at houseofdata.in/verify without an account.
The tool chain, not a demo of it Python, SQL, LangChain, LangGraph, vector databases, Docker, Kubernetes and the three clouds.
Every class recorded in your portal the same day, with the notes and the assignment attached. Miss one and you have not lost it.
Fifteen students, never more small enough that a trainer knows where each person is stuck. This is the number the whole thing is built around.
Placement support until you are placed not for ninety days. Until. The conditions are in writing before you pay anything.
Prerequisites
Who the Flagship is for.
Six months, starting at the beginning. It is the longer road on purpose, and it is the wrong one for some people.
This is for you if
- You are a fresher, or coming out of a career gap
- You are from a non-IT background and starting from zero
- You have never written code and want to be taught it properly
- You want the maths taught rather than skipped
- You would rather have room to absorb it than finish sooner
Look elsewhere if
- You already write Python daily — take the Fast Track
- You need to be job-ready in under three months
- You want a certificate more than you want the projects
- You cannot give it the hours — this is not a watch-later course
Fast Track or flagship
The honest comparison.
Same subject, same trainers, same placement cell. What changes is how much ground gets covered and how much room there is to absorb it.
| Fast Track | Flagship | |
|---|---|---|
| Length | 3 months (2 training + 1 placement) | 6 months |
| Course fee | ₹59,999 + post-placement fee | ₹99,999 + post-placement fee |
| Coding background | Some coding assumed | None needed — starts at the beginning |
| Python & SQL | 2 weeks, revision pace | 5 weeks, from scratch with practice time |
| Maths & statistics | Folded into the ML week | Its own module — probability, statistics, linear algebra |
| Machine learning | Foundations | Supervised, unsupervised, recommenders, deep learning |
| Gen AI, RAG, agents | Full coverage | Full coverage, with more build time |
| Multi-agent systems | Introduced | Built and deployed |
| Fine-tuning | — | Included |
| MLOps & LLMOps | Deployment basics | Full module — monitoring, evaluation, guardrails, cost |
| Capstone projects | 1 deployed | 4 deployed — 2 real-time builds, RAG, document extraction |
| Placement support | Dedicated month, continues until placed | Extended, continues until placed |
| Best for | Career switchers on a deadline | Freshers, career gaps, non-IT backgrounds |
The syllabus
Fifteen modules, in the order they are useful.
Twenty-four weeks of taught content, grouped into five phases. No module starts before the one under it is solid — that is the whole reason this version takes six months. Every module, in full →
- Modules 01–04 · 6 weeksFoundations Where Gen AI, ML and deep learning actually sit. Python and vibe coding. SDLC and AIDLC. Then data properly — DBMS, how SQL really works, and everything that is not a table.
- Modules 05–06 · 3 weeksThe models themselves Transformer architecture, LLMs against SLMs, embedding models, fine-tuning and when not to. Then prompting, context engineering, evaluation sets and guardrails.
- Modules 07–11 · 10 weeksBuilding RAG and vector databases, document extraction, the types of chatbot, KAG. Agents and orchestration with LangChain, LangGraph, LangSmith, MCP and n8n. Coding with Copilot, Cursor and Kiro, a module on Claude Code and Cowork of its own, then the product around the model — FastAPI, GitHub, React or Angular, and UX in Figma.
- Modules 12–13 · 5 weeksProduction Docker, Kubernetes, Jenkins and CI/CD. Then the cloud services that appear in the job ads: AWS Bedrock, Textract, EKS, ECS, SQS and S3; Azure Document Intelligence, AI Search, Key Vault and DevOps; Vertex AI on GCP; Cloudflare Workers and Pages.
- Modules 14–15Proof, then the job Two mainstream real-time projects, an end-to-end RAG system and a document extraction pipeline, all deployed. Then profile building and interviews, which carry on past the end of the syllabus. How this works →
What you leave with
Four deployed projects and a profile that reads like an engineer's.
4 real projects
Two machine learning, two Generative AI, all deployed to Azure, AWS or GCP on public URLs. Not notebooks — running systems you can hand an interviewer.
Certification and lifetime access
Certification on completion, lifetime access to House of Data, free webinars, and an internship opportunity while you study.
The full placement programme
Resume rewrite, Naukri and LinkedIn rebuilt, mock interviews and hiring-partner submissions — extended, and running until you are placed.
The course fee is ₹99,999 plus a separate post-placement fee payable once you are earning. Instalments are available. For the post-placement figure and any offer running when you join, talk to the team — we would rather quote you something current than publish a number that goes stale.
Not sure which one you need?
Come to a free demo class and ask. Fifteen minutes with a trainer is worth more than an hour of reading comparison tables, and nobody will push you toward the more expensive one.