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Live Online Cohort · June 2026

Building Production
AI Systems

A 3-part hands-on program covering Design → Develop → Deploy for real-world AI systems.

Upcoming cohort

June 2026 Live Classes

📅 Schedule

Starts
Morning Batch 8:00 AM – 10:00 AM IST · Saturdays & Sundays
Evening Batch 8:00 PM – 10:00 PM IST · Saturdays & Sundays

You'll receive invites to both batches: choose which one to join each day.

₹55,000

One-time investment · Lifetime access · Full 3-part track

12-week program

Program Outline

Click any week to expand the details.

Saturday

AI Engineering Landscape

  • •AI Engineer vs ML Engineer
  • •LLM Applications Architecture
  • •Context Windows
  • •Tokens
  • •Cost
  • •Latency
  • •Hallucinations

Workshop: Build a basic prompt → LLM → response pipeline

Sunday

Development Environment

  • •UV
  • •OpenAI SDK
  • •LiteLLM
  • •Environment Management
  • •Project Structure

Assignment: Build a basic AI Assistant

Tools & Frameworks

UV OpenAI SDK LiteLLM

Saturday

Structured Generation

  • •JSON Mode
  • •Schemas
  • •Validation
  • •Pydantic

Workshop: Invoice Parser

Sunday

Reliability Engineering

  • •Validation Failures
  • •Retry Strategies
  • •Error Handling

Assignment: Support Ticket Classifier

Tools & Frameworks

Pydantic OpenAI SDK

Saturday

DSPy Foundations

  • •Signatures
  • •Modules
  • •Chains

Workshop: DSPy Classifier

Sunday

Prompt Optimization

  • •Examples
  • •Teleprompting
  • •Optimizers

Assignment: Multi-stage Extraction Workflow

Tools & Frameworks

DSPy

Saturday

Why Evaluation Matters

  • •Golden Datasets
  • •Benchmarking
  • •Human Evaluation

Workshop: Build Evaluation Dataset

Sunday

Automated Evaluation

  • •DeepEval
  • •Ragas

Assignment: Evaluate Previous Projects

Tools & Frameworks

DeepEval Ragas

Saturday

Production Monitoring

  • •Traces
  • •Logs
  • •Metrics

Workshop: Phoenix

Sunday

Cost & Latency

  • •Token Usage
  • •Monitoring
  • •Dashboards

Assignment: Observability Dashboard

Tools & Frameworks

Phoenix LangSmith

Saturday

Why RAG Exists

  • •Embeddings
  • •Similarity Search
  • •Retrieval

Workshop: Dense Retrieval

Sunday

Chunking

  • •Fixed
  • •Recursive
  • •Semantic

Assignment: Build Naive RAG

Tools & Frameworks

FAISS Qdrant

Saturday

Hybrid Search

  • •BM25
  • •Dense Retrieval
  • •RRF

Workshop: Hybrid Retriever

Sunday

Retrieval Optimization

  • •Reranking
  • •Query Rewriting
  • •Context Compression

Assignment: Production RAG Pipeline

Tools & Frameworks

BM25 Qdrant Cohere Rerank

Saturday

Agent Fundamentals

  • •Tool Calling
  • •Planning
  • •State

Workshop: Core concepts of Agent

Sunday

LangGraph

  • •Nodes
  • •Edges
  • •Workflows

Assignment: Research Agent

Tools & Frameworks

LangGraph OpenAI

Saturday

Agent Collaboration

  • •Supervisors
  • •Delegation
  • •Routing

Workshop: Multi-Agent Workflow

Sunday

Real Architectures

  • •Customer Support
  • •Research
  • •Operations

Assignment: Multi-Agent Project

Tools & Frameworks

LangGraph CrewAI

Saturday

Memory

  • •Short-Term
  • •Long-Term

Workshop: Memory Layer

Sunday

Context Management

  • •Compression
  • •Summarization
  • •Retrieval Memory

Assignment: Persistent Assistant

Tools & Frameworks

LangMem Redis PostgreSQL

Saturday

Security

  • •Prompt Injection
  • •Jailbreaks
  • •PII

Workshop: Attack Simulations

Sunday

Guardrails

  • •NeMo Guardrails
  • •Guardrails AI

Assignment: Secure Agent

Tools & Frameworks

NeMo Guardrails Guardrails AI

Saturday

Deployment

  • •FastAPI
  • •Docker
  • •Cloud Deployment

Workshop: Production Deployment

Sunday

Final Project Presentations

  • •Enterprise AI Assistant
  • •Structured Outputs → DSPy → Evaluation → Monitoring → RAG → Agent → Memory → Guardrails → Deployment

Tools & Frameworks

FastAPI Docker Cloud

What Students Receive

By the end of 12 weeks they will have:

10+ portfolio projects
Production RAG system
LangGraph Agent
Evaluation framework
Monitoring stack
Memory system
Guardrails implementation
Deployment experience
Capstone project

What you will get

🏗️

Design → Develop → Deploy for production AI systems

⚖️

Trade-offs in model selection & pipelines

🎥

Lifetime access to recordings & community

💻

Prerequisite: Programming experience required

Who's teaching

Instructors

AK

Dr. Arun Kumar

Instructor · 15+ years experience

Arun previously led AI teams at Eneco and Regnology, scaling AI systems to millions of users. 15+ years building production ML & AI applications across enterprise and cloud platforms.

MCA · PhD in Computer Science, Central University of Haryana