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Cohort starts from 28th June

Become top 1% in GenAI Development

In 7 weeks you'll have built and deployed 6 production GenAI applications from finetuned LLMs to autonomous agents, ready to show in any interview or client pitch.

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Trusted by 1,000+ Practitioners

Software Engineers · Data Scientists · Architects · CTOs · Project Managers

6+

Live Projects

15+

Industry Tools

10+

Core Skills

7

Weeks · Flexible

1:1

Live Projects

Most

courses

teach

you

concepts.

This

one

ships

GenAI products.

Unlike video-only platforms, every module ends with a deployed, working application. Unlike bootcamps, you get dedicated 1-on-1 mentor access throughout. Unlike generic programs, this curriculum is built around the exact skills appearing in real GenAI job descriptions today.

Project-Centric

Every module ends with a deployed app not a Jupyter notebook.

Industry Relevant

Real business use cases, real datasets, real constraints.

Expert Guided

Kaggle Grandmasters and enterprise AI veterans.

Portfolio Ready

6+ projects any hiring manager can evaluate immediately.

Detailed Curriculum

6 Projects. End-to-End GenAI

6 Modules.

End-to-End GenAI

Every concept is paired with a production deployment. 6 weeks of content, 7 weeks of building.

MOD

Topic & Focus

Key Tools

Project(s)

LLM Foundations & Playgrounds

Finetuning · Prompt Engineering · Model Selection · Cost Management · Model Benchmarking

Customer Support Assistant

LLM Foundations & Playgrounds

Finetuning · Prompt Engineering · Model Selection · Cost Management · Model Benchmarking

Customer Support Assistant

Finetuning for Real Use Cases

LoRA · QLoRA · PEFT · PPO · DPO · GRPO · Unsloth AI

DPO Fine-tuned Enterprise Security Compliance LLM Application

Finetuning for Real Use Cases

LoRA · QLoRA · PEFT · PPO · DPO · GRPO · Unsloth AI

DPO Fine-tuned Enterprise Security Compliance LLM Application

Retrieval-Augmented Generation

Vector DBs · Hybrid Search · HyDE · Embeddings Model · Re-ranking · RAG Evaluation

Optimized RAG Application for Legal Query Resolution

Retrieval-Augmented Generation

Vector DBs · Hybrid Search · HyDE · Embeddings Model · Re-ranking · RAG Evaluation

Optimized RAG Application for Legal Query Resolution

AI Agents

ReAct · Multi-Agent · Agentic RAG · Memory · Tool Design · MCP · A2A Prototcol · LangGraph

Agentic AI based Contract Document Drafter

AI Agents

ReAct · Multi-Agent · Agentic RAG · Memory · Tool Design · MCP · A2A Prototcol · LangGraph

Agentic AI based Contract Document Drafter

Deployment & Evaluation

AWS · vLLM · Ollama · DeepEval · TrueLens · TTFT · MLFlow · Docker · FastAPI · Streamlit

Conversational BI App

Deployment & Evaluation

AWS · vLLM · Ollama · DeepEval · TrueLens · TTFT · MLFlow · Docker · FastAPI · Streamlit

Conversational BI App

LLMOps

Pipeline Tracking · Guardrails · Incident Response · Grafana ·Prometheus

🏆 Capstone: Perplexity-style Application

LLMOps

Pipeline Tracking · Guardrails · Incident Response · Grafana ·Prometheus

🏆 Capstone: Perplexity-style Application

LLM Foundations & Playgrounds

Master the Core Before You Build

Concepts Covered

Transformers, attention mechanisms, and tokenization

Model benchmarks: MMLU, HumanEval, MT-Bench - how to read them

Model selection: cost, latency, and quality trade-offs

Prompt engineering: zero-shot, few-shot, chain-of-thought, ReAct

System prompt architecture for production reliability

Open-source vs closed-source, when and why

Token budgeting and API cost management from day one

Outcome

You can evaluate, select, and prompt any LLM for a given business requirement and justify that decision to a technical team.

Tools

OpenAI

Claude

Gemini

Llama

HuggingFace

Project 01

Customer Support Assistant

Build an LLM-powered assistant that answers customer queries with reliable prompts, optimized responses, and cost aware design,

LLM Foundations & Playgrounds

Master the Core Before You Build

Concepts Covered

Transformers, attention mechanisms, and tokenization

Model benchmarks: MMLU, HumanEval, MT-Bench - how to read them

Model selection: cost, latency, and quality trade-offs

Prompt engineering: zero-shot, few-shot, chain-of-thought, ReAct

System prompt architecture for production reliability

Open-source vs closed-source, when and why

Token budgeting and API cost management from day one

Outcome

You can evaluate, select, and prompt any LLM for a given business requirement and justify that decision to a technical team.

Tools

OpenAI

Claude

Gemini

Llama

HuggingFace

Project 01

Customer Support Assistant

Build an LLM-powered assistant that answers customer queries with reliable prompts, optimized responses, and cost aware design,

Finetuning for Real Use Cases

Own the Model, Not Just the Prompt

Concepts Covered

When to finetune vs RAG vs prompting - the decision framework

Quantization: INT4, INT8, GPTQ, GGUF - memory and speed trade-offs

Dataset design - structure, quality signals, data cleaning pipelines

Supervised Finetuning (SFT) from scratch

LoRA, QLoRA, PEFT - how adapters work under the hood

PPO - Proximal Policy Optimization (RLHF)

DPO - Direct Preference Optimization (simpler, more stable alignment)

ORPO - Odds Ratio Preference Optimization (single-stage finetuning + alignment)

Unsloth AI - 2× faster training, 60% less memory

Evaluation: loss curves, perplexity, human rubrics

Outcome

You can design a finetuning pipeline end-to-end dataset to deployment - for any domain-specific use case.

Tools

Unsloth AI

HF PEFT

W&B

Modal

Project 02

Personalized Content Generator

Finetune an open-source LLM using QLoRA. Configurable tone, format, and length. Deployed as REST API with structured output.

Project 03

DPO Fine-tuned Enterprise Security Compliance LLM Application

Fine-tune and align an LLM to generate secure, consistent, and policy-compliant responses for enterprise security use cases.

Finetuning for Real Use Cases

Own the Model, Not Just the Prompt

Concepts Covered

When to finetune vs RAG vs prompting - the decision framework

Quantization: INT4, INT8, GPTQ, GGUF - memory and speed trade-offs

Dataset design - structure, quality signals, data cleaning pipelines

Supervised Finetuning (SFT) from scratch

LoRA, QLoRA, PEFT - how adapters work under the hood

PPO - Proximal Policy Optimization (RLHF)

DPO - Direct Preference Optimization (simpler, more stable alignment)

ORPO - Odds Ratio Preference Optimization (single-stage finetuning + alignment)

Unsloth AI - 2× faster training, 60% less memory

Evaluation: loss curves, perplexity, human rubrics

Outcome

You can design a finetuning pipeline end-to-end dataset to deployment - for any domain-specific use case.

Tools

Unsloth AI

HF PEFT

W&B

Modal

Project 02

Personalized Content Generator

Finetune an open-source LLM using QLoRA. Configurable tone, format, and length. Deployed as REST API with structured output.

Project 03

DPO Fine-tuned Enterprise Security Compliance LLM Application

Fine-tune and align an LLM to generate secure, consistent, and policy-compliant responses for enterprise security use cases.

Retrieval-Augmented Generation (RAG)

Give Your LLM a Memory It Can Trust

Concepts Covered

End-to-end RAG: ingestion → chunking → embedding → retrieval → generation

Vector DBs: when to use Chroma vs Pinecone vs Qdrant

Embedding models: dense vs sparse, domain selection criteria

Chunking strategies: fixed-size, semantic, hierarchical

Pre-retrieval: Sentence window retrieval, HyDE (Hypothetical Document Embeddings)

Retrieval: Hybrid search — BM25 + dense ensemble retrievers

Post-retrieval: Cross-encoder re-ranking, LLM-based re-ranking

RAG evaluation: faithfulness, answer relevance, context precision

Outcome

You can architect, optimize, and evaluate a production RAG pipeline and diagnose exactly where it fails.

Tools

LlamaIndex

LangChain

Chroma

Pinecone

Qdrant

DeepEval

Project 04

Enterprise Knowledge Base Assistant

RAG system over research papers or internal documents. All three retrieval optimization layers. Evaluated on faithfulness, relevance, and context quality. Deployed with Chainlit chat interface.

Retrieval-Augmented Generation (RAG)

Give Your LLM a Memory It Can Trust

Concepts Covered

End-to-end RAG: ingestion → chunking → embedding → retrieval → generation

Vector DBs: when to use Chroma vs Pinecone vs Qdrant

Embedding models: dense vs sparse, domain selection criteria

Chunking strategies: fixed-size, semantic, hierarchical

Pre-retrieval: Sentence window retrieval, HyDE (Hypothetical Document Embeddings)

Retrieval: Hybrid search — BM25 + dense ensemble retrievers

Post-retrieval: Cross-encoder re-ranking, LLM-based re-ranking

RAG evaluation: faithfulness, answer relevance, context precision

Outcome

You can architect, optimize, and evaluate a production RAG pipeline and diagnose exactly where it fails.

Tools

LlamaIndex

LangChain

Chroma

Pinecone

Qdrant

DeepEval

Project 04

Enterprise Knowledge Base Assistant

RAG system over research papers or internal documents. All three retrieval optimization layers. Evaluated on faithfulness, relevance, and context quality. Deployed with Chainlit chat interface.

AI Agents

From Answering Questions to Taking Action

Concepts Covered

Agent architecture: reasoning loops, tool use, memory, planning

ReAct (Reasoning + Acting) pattern - how agents decide what to do next

Tool design: building reliable, composable tools for agents

Single-agent systems: design, prompt architecture, failure modes

Multi-agent: orchestrator-worker patterns, specialization, protocols

Agentic RAG - combining retrieval with autonomous decision-making

Memory systems: short-term, long-term (vector store), entity memory

Failure handling: fallbacks, retries, human-in-the-loop checkpoints

Agent evaluation: trajectory evaluation, task completion rate, cost per task

Outcome

You can design and deploy multi-agent systems that autonomously complete complex, multi-step tasks.

Tools

LangGraph

CrewAI

MCP

LlamaIndex

A2A

Memory Systems

Project 06

Conversational Business Intelligence App

You can take any LLM app from prototype to a monitored, evaluated, production API with benchmarks to back it up.

AI Agents

From Answering Questions to Taking Action

Concepts Covered

Agent architecture: reasoning loops, tool use, memory, planning

ReAct (Reasoning + Acting) pattern - how agents decide what to do next

Tool design: building reliable, composable tools for agents

Single-agent systems: design, prompt architecture, failure modes

Multi-agent: orchestrator-worker patterns, specialization, protocols

Agentic RAG - combining retrieval with autonomous decision-making

Memory systems: short-term, long-term (vector store), entity memory

Failure handling: fallbacks, retries, human-in-the-loop checkpoints

Agent evaluation: trajectory evaluation, task completion rate, cost per task

Outcome

You can design and deploy multi-agent systems that autonomously complete complex, multi-step tasks.

Tools

LangGraph

CrewAI

MCP

LlamaIndex

A2A

Memory Systems

Project 06

Conversational Business Intelligence App

You can take any LLM app from prototype to a monitored, evaluated, production API with benchmarks to back it up.

Deployment & Evaluation of LLMs

Production Is Where Good Intentions Go to Die

Concepts Covered

The production gap: why notebook models fail in production

API deployment patterns: REST, streaming, async

Ollama: on-premise deployment for privacy-sensitive use cases

vLLM: high-throughput serving with PagedAttention for enterprise scale

Amazon Bedrock: managed cloud with enterprise SLAs

Containerization fundamentals for LLM applications

DeepEval: LLM unit testing, regression testing pipelines

TrueLens: RAG-specific evaluation, feedback functions

Performance benchmarking: TTFT, throughput, p95 latency

Cost optimization: batching, caching, model routing

Outcome

You can take any LLM app from prototype to a monitored, evaluated, production API with benchmarks to back it up.

Tools

vLLM

Ollama

AWS

FastAPI

DeepEval

TrueLens

Project 06

Conversational Business Intelligence App

Natural language → SQL → tables → charts. Evaluated on output accuracy. Full-stack: Streamlit frontend, FastAPI backend, deployed to cloud.

Deployment & Evaluation of LLMs

Production Is Where Good Intentions Go to Die

Concepts Covered

The production gap: why notebook models fail in production

API deployment patterns: REST, streaming, async

Ollama: on-premise deployment for privacy-sensitive use cases

vLLM: high-throughput serving with PagedAttention for enterprise scale

Amazon Bedrock: managed cloud with enterprise SLAs

Containerization fundamentals for LLM applications

DeepEval: LLM unit testing, regression testing pipelines

TrueLens: RAG-specific evaluation, feedback functions

Performance benchmarking: TTFT, throughput, p95 latency

Cost optimization: batching, caching, model routing

Outcome

You can take any LLM app from prototype to a monitored, evaluated, production API with benchmarks to back it up.

Tools

vLLM

Ollama

AWS

FastAPI

DeepEval

TrueLens

Project 06

Conversational Business Intelligence App

Natural language → SQL → tables → charts. Evaluated on output accuracy. Full-stack: Streamlit frontend, FastAPI backend, deployed to cloud.

LLMOps

The Work Doesn't End at Deployment

Concepts Covered

LLMOps lifecycle: experiment tracking to production monitoring

Pipeline tracking with MLflow and Weights & Biases

Model versioning and reproducibility

Token usage monitoring: cost attribution by user, feature, and team

Prompt versioning and A/B testing in production

Data drift and model drift - detection and response strategies

LLM Guardrails: prompt injection, PII filtering, toxicity, brand safety

Ethical AI pipelines: fairness, transparency, accountability frameworks

Incident response: runbooks and rollback strategies

Outcome

You can design LLMOps infrastructure that keeps a production AI system reliable, observable, safe, and cost-efficient over time.

Tools

MLflow

Grafana

Prometheus

DeepEval

FastAPI

Cerebrium

Project 01

Customer Support LLM

Benchmark models, design system prompts, deploy a working API endpoint. Evaluated on accuracy, latency, and cost.

LLMOps

The Work Doesn't End at Deployment

Concepts Covered

LLMOps lifecycle: experiment tracking to production monitoring

Pipeline tracking with MLflow and Weights & Biases

Model versioning and reproducibility

Token usage monitoring: cost attribution by user, feature, and team

Prompt versioning and A/B testing in production

Data drift and model drift - detection and response strategies

LLM Guardrails: prompt injection, PII filtering, toxicity, brand safety

Ethical AI pipelines: fairness, transparency, accountability frameworks

Incident response: runbooks and rollback strategies

Outcome

You can design LLMOps infrastructure that keeps a production AI system reliable, observable, safe, and cost-efficient over time.

Tools

MLflow

Grafana

Prometheus

DeepEval

FastAPI

Cerebrium

Project 01

Customer Support LLM

Benchmark models, design system prompts, deploy a working API endpoint. Evaluated on accuracy, latency, and cost.

What You Actually Learn

vs What Employers Need

What You Actually Learn

vs What Employers Need

Every row maps to a skill appearing in real GenAI job descriptions at TCS, Barclays, Accenture, and EY.

Curriculum Depth

LLM foundations & benchmarks

Finetuning — LoRA / QLoRA / PEFT

Alignment — DPO / ORPO / PPO

RAG — full production pipeline

Hybrid search & re-ranking

HyDE & advanced retrieval strategies

Multi-agent systems

Agent Memory and Tool Design

LLMOps — monitoring & guardrails

vLLM / Ollama production serving

Industry Readiness

JD alignment score

Skills from real JD requirements

Deployed portfolio projects

Tools employers actually use

Price

TMLC

BEST

92%

12/12

6+ Apps

15+ Tools

₹6,999

IISc

58%

7 / 12

1 Capstone

8-10 Tools

₹1,60,000

IIT KGP

50%

6 / 12

1 Capstone

8-10 Tools

₹1,80,000

IIIT Hyd

42%

5/ 12

1 Capstone

6-8 Tools

₹1,40,000

IIT Madras

33%

4 / 12

Case Studies

4-6 Tools

₹3,20,000

upgrad

17%

2 / 12

Case Studies

4-6 Tools

₹2,99,000

Fully covered

Partially covered

Not covered

Technology Stack

15+ Industry Tools

Current, production-relevant — the same tools appearing in real job descriptions.

Large Language Models

Vector Databases

Agentic and Data Frameworks for LLMs

Training, Inference and Serving LLMs

LLMOps, LLM Evaluation and Deployment

Course mentors

Learn From Practitioners

Not just instructors. Learn from Kaggle-ranked practitioners who design, deploy, and scale AI systems for real business use cases.

🏆 Kaggle Grandmaster

Saurabh Shahane

Founder & CEO, The Machine Learning Company

9+ years building enterprise Data Science, GenAI, and AI solutions across multiple domains

10,000+ professionals impacted through outcome-driven learning programs

Consulted global enterprises and startups on scalable Enterprise AI solutions

Built this program around hands-on, production-first learning

🏆 Kaggle Master

Chirag Chauhan

ML Engineer, The Machine Learning Company

5+ years in ML/GenAI — specializing in finetuning, deploying, and serving LLM-powered solutions

Expert in agentic AI architectures integrating LLMs, tools, and orchestration layers

Experienced MLOps practitioner focused on scalable, reliable AI systems

Mentored 1,000+ learners in ML, GenAI, LLM deployment, and MLOps

Success stories From the program

What Practitioners Say

MGP program from TMLC was an amazing experience. I came to learn Explainable AI and brush up my Deep learning skills - thoroughly enjoyed it! The mentors are great at tutoring. This worthy course builds projects on real world business use cases. If you're consistent, you'll get the BEST out of this course!
Vetrivel PS
Lead NLP Eng, Genpact
I registered for Guided Projects because I wanted real-time DL projects with team deadlines - TMLC provided exactly what I needed. I gained skills like end-to-end ML Pipeline, Streamlit integration, deployment, explainable AI, and MLOps. I recommend being part of it and keep learning.
Sandeep Kirwai
Data Scientist II, Pattern
This has been an insightful learning curve for me. I learnt a lot more concepts and I got to understand the already learnt ones more deeply especially on modeling. I’m more confident in engaging in more projects. This was actually my first attempt of doing a project post the capstone project.
Natash Nalyaka
Data Analyst, Intelligra
I had a wonderful experience with TMLC Academy's guided projects program. The mentors are extremely professional and has a vast knowledge of the field and thus is able to provide with the right guidance wherever necessary. I would recommend it to anyone who is interested.
Manasvi Logani
Data Scientist, Newgen Software
This program introduced me to new parts of deep learning like Explainable AI, MLOps and deployment. It boosted my confidence on Deep learning models and improved my teamwork skills. TMLC's guided project is the perfect place for a head start on deep learning applications.
Kabilan N
Computer Vision Engineer, The ePlane Company
This is a wonderful program with hands-on data science projects. I got great exposure working with real datasets and deploying ML models to cloud resources. The TMLC team has rock solid knowledge and expertise to guide on real-time issues. Every mentor session was really helpful with lots of insights.
Sandeep Raj
Software Engineer, Doodle Labs

what’s included

One Price. Complete Access.

One Price.

Complete Access.

Live Cohort

Flexible scheduling around your work calendar

Live 1:1 Support

Personal mentor access throughout the program

Certificate

Industry-recognized, LinkedIn-ready credential

GenAI Jobs Roadmap

For professional advancement and role transitions

2 Years Access

All materials, recordings, and updates

Job Opportunities

Referrals and research opportunities in select cases

Community Access

Professional network of AI practitioners

6+ Portfolio Apps

Production-grade, deployed, interview-ready

Enrollment

Ready to invest in Yourself?

GenAI Program

6,999

14,000

Early Bird 50% Discount

20/ 50 seats filled

Only 30 spots left at this price

6+ Production-grade portfolio projects

Live 1:1 doubt support from expert mentors

Industry-recognized Certificate of Completion

Careeer Roadmap for GenAI Opportunities

2 years access to all course materials

Professional AI community access

Job/ research opportunities (selected cases)

Enroll Now

TMLC was Selected by Stanford

Graduate School of Business

TMLC was Selected by Stanford

Graduate School of Business

TMLC Academy was selected for the prestigious Seed Spark Program — chosen from 140+ startups across South Asia by the Stanford Graduate School of Business. A formal validation of educational excellence and innovation.

Selected from 140+ startups across South Asia

6th cohort of the Seed Spark Program

Recognised for educational excellence and innovation

Validation from one of the world's top business schools

FAQs

Before you Enroll

Is there any pre-requisite required before or during the program

Intermediate level python programming is required. GenAI pre-requisites are covered in the program.

How the sessions will be organized. Will the recording be available if I miss any class?

This program contains both pre-recorded and live sessions. Live sessions will happen on weekends. Recordings will be provided after the live session.

What will be the duration of the program?

This is a 6 weeks long program.

What materials will be provided during the course?

Videos, documents, coding assignments will be provided during the program.

What will be the timings of the sessions?

Live sessions will happen 6 to 7:30 PM IST on weekends.

Is the fee refundable?

Program fee is non refundable.

Your next role in AI starts

with your first deployed project.

Your next role in AI starts with your first deployed project.

Join 1,000+ software engineers, data scientists, architects, and CTOs who are already building the future of enterprise AI.

Enroll Now - ₹6,999

Become top 1% in GenAI Development

6+

15+

10+

7

1:1

courses

courses

Ready to invest in Yourself?

6,999

TMLC was Selected by Stanford

Graduate School of Business

TMLC was Selected by Stanford

Graduate School of Business

Before you Enroll

Your next role in AI starts

with your first deployed project.

Guided Projects in Gen-AI