Build AI systems that work beyond the demo.
Practical programs, live online cohorts and corporate training for developers who ship AI into production: agents, MCP, RAG and machine learning.
The demo
In production
- Input
- Guardrails
- Retrieval
- Agent
- Evaluate
- Monitor
- Result
The model is one box. The engineering is everything around it.
Engineers working here are also trained from us.
- ISRO
- Broadridge
- TCS
- Microsoft
- Bosch
- Wipro
Learn by building
From a working idea to an engineered system.
prompt
→ model.generate()
→ structured output ✓
01
Learn the pieces
Self-paced programs and tutorials that take you from LLM basics to agents and MLOps, with the fundamentals explained clearly.
Online · Self-paced
Explore programs ↗02
Build with guidance
Live online cohorts where you build real AI systems with an instructor, in small groups, and get feedback on your work.
Live online · Cohorts
See live cohorts ↗03
Grow team capability
Corporate programs shaped around your developers, your stack and your engineering goals.
Corporate · Custom
Discuss team training ↗Focus areas
Nine areas, in the order you learn and ship them.
Start with models and generative AI, build with retrieval and agents, then evaluate and operate what you ship.
Foundations
- 01
Machine learning and deep learning
Core models, training and evaluation, from regression to neural networks.
- 02
Computer vision
Image classification, detection and segmentation with modern vision models.
- 03
Generative AI foundations
How LLMs work: tokens, transformers, prompting and structured outputs.
Build
- 04
RAG and LLM engineering
Retrieval, embeddings and pipelines that ground answers in your data.
- 05
AI agents and agentic AI
Tool-using agents that plan, act and verify across multi-step tasks.
- 06
MCP and tool integration
Connect models to tools and data through the Model Context Protocol.
Ship and scale
- 07
Evaluation and guardrails
Test sets, LLM evals and safety checks that keep outputs reliable.
- 08
System design for AI
Architecting reliable, scalable LLM and agent systems.
- 09
MLOps and LLMOps
Deploying, monitoring and evaluating models and LLM apps in production.
The stack
Tools we teach with.
Build models
Python
Language and tooling
PyTorch
Deep learning
NumPy
Arrays and maths
pandas
Data wrangling
scikit-learn
Classical ML
OpenCV
Computer vision
Hugging Face
Models and datasets
+ More
Jupyter · TensorFlow · ONNX
Build LLM apps
LangChain
LLM app building
LangGraph
Agent workflows
MCP
Tool protocol
Pydantic
Typed tool schemas
FastAPI
Serve agents as APIs
Qdrant
Vector search
Claude API
Model APIs
+ More
Ollama · Streamlit · Gradio · Redis
Ship and operate
Docker
Package and deploy
Git
Version control
GitHub Actions
CI and deployment
pytest
Evaluation tests
MLflow
Track and manage models
Weights & Biases
Experiment tracking
+ More
Kubernetes · Grafana · Linux · cloud platforms
How we teach
Understand it, build it, test it, ship it.
Every program follows the same four steps, whether it is a 3-hour workshop or a team cohort.
Step 1
Understand
Fundamentals first
Each topic starts with how it works: models, retrieval, APIs and software design, before any framework.
You can explain it.
Step 2
Build
Build it live
Instructor-led sessions where you write and run the code, with real tools and real data.
You have it working.
Step 3
Test
Test the output
Test sets, LLM evals and guardrails are part of the exercise, not an afterthought.
You know it is reliable.
Step 4
Ship
Ship and operate
Deployment, monitoring and cost are in the curriculum, so your project runs beyond the demo.
It runs in production.
Upcoming programs
Start with a workshop. Go deeper with a cohort.
Two live online programs, built to be taken in order.
Build a Real AI Agent with Python
Developer-focused, 3 hours. Build an AI Operations Assistant that calls tools, handles failures and returns structured answers.
- Format
- Live online
- Duration
- 3 hours
Next batch
Saturday, 28th Nov 2026
6:00 PM to 9:00 PM IST
12:30 to 15:30 UTC · convert to your time zone
You will build
- 01A working AI Operations Assistant
- 02A controlled agent loop
- 03Reliability you can reason about
₹499 + GST
View workshop details ↗AI Agents Engineering in Production
Tools, MCP, RAG, LangGraph, FastAPI, evaluation and deployment.
- Format
- Live online
- Duration
- 25 hours · 10 days × 2.5 h
Next batch
Mon 7th Dec to Fri 18th Dec 2026
Monday to Friday, no weekends
7:00 PM to 9:30 PM IST
13:30 to 16:00 UTC · convert to your time zone
You will build
- 01Agents with tools and MCP
- 02RAG and LangGraph workflows
- 03A deployed, evaluated service
₹7,499 + GST
Explore cohort ↗Corporate training
Build AI capability across your team.
Live online programs shaped around your developers, your stack and your engineering goals.
- 01Plan
Custom curriculum
Mapped to your stack, roles and current skill level.
- 02Learn
Live online cohorts
Instructor-led, hands-on, with real project work.
- 03Build
Capstone and review
Team builds a working system, reviewed by engineers.
- 04Support
Follow-up support
Office hours and Q&A after the programme ends.
Experience learning AI, Coming Soon
Daily AI practice, with BIT.
Short, focused practice that keeps your AI engineering skills sharp between workshops.
Why an AI agent needs evaluation, not just a good prompt
A short read with a code sample, key takeaways and a link to the full lesson.
- Agents
- Evaluation
- Python
Flashcard
What does a tool call return to the model?