Indian Institute of Technology Kanpur

AI Engineering & Infrastructure

Indian Institute of Technology Kanpur

Artificial Intelligence & Machine Learning

Application Deadline Approaching

Last date to apply: October 30, 2026 · Program starts on November 1, 2026

About Programme

Learn about LLMs, RAG, AI agents, as well as model deployment and cloud infrastructure by following guided projects.
Learn the process by which AI models are constructed, then deployed, monitored, and implemented in real-world production environments.
From modeling and data through deployment and monitoring, as well as AI infrastructure.
Learn about containers, GPUs, cloud infrastructure, and orchestration. Also, learn about auto-scaling through hands-on demonstrations.
Learn about model serving strategies for rollout, edge inference, and monitoring, and the reliability of AI systems.
Industry experts will be presenting, along with exclusive IIT/NIT masterclasses by faculty, and an end-of-course closing.
Develop expertise in cloud deployment, cloud infrastructure Monitoring, AIOps, and AI design of systems.

What you'll learn

Master Prompt Engineering, Retrieval-Augmented Generation (RAG), and AI Agents to Build Production-Ready AI Applications.
Understand How GPUs, Cloud Platforms, Containers, Orchestration, and Auto-Scaling Power Modern AI Infrastructure.
Gain Practical Knowledge of AI Monitoring, Model Drift, CI/CD, AIOps, and Troubleshooting Production AI Workloads.
Build AI Deployment and AI Agent Projects Through Guided, Instructor-Led Hands-On Templates.
Understand AI Security, Bias, Fairness, Privacy, Governance, and Cost Optimization for Responsible AI Systems.
Design an End-to-End AI Solution Demonstrating Deployment, Monitoring, Governance, and Operational Best Practices.

Skills you'll gain

AI Engineering Fundamentals Large Language Model (LLM) ApplicationsRetrieval-Augmented Generation (RAG)Prompt EngineeringAI Agent Fundamentals AI Model Evaluation AI Model Packaging & VersioningAI Model DeploymentAI Serving & InferenceAI Infrastructure FundamentalsCloud Infrastructure for AIGPU & Compute FundamentalsContainers & OrchestrationInfrastructure as CodeAI Monitoring & ObservabilityModel Drift DetectionAIOps FundamentalsCI/CD Concepts for AIResponsible AI (Bias, Fairness & Privacy)

Programme Content

Topics Covered

  • Understand the Role of an AI Engineer Compared with Data Science and AI Research.
  • Explore the End-to-End AI Lifecycle from Raw Data to a Production-Ready AI System.
  • Understand the Difference Between AI Training and Inference Through Practical Examples.
  • Learn How AI Systems Are Evaluated for Performance and Reliability.
  • Understand Why AI Operations and Production Reliability Are Critical.
  • Guided Hands-On: Walk Through a Complete AI Workflow from Dataset to a Working, Evaluated AI System.

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    Topics Covered

  • Understand How AI Models Are Packaged for Applications and Services.
  • Learn the Basics of Hosting and Serving AI Models.
  • Explore Model Versioning and Experiment Tracking.
  • Understand the Fundamentals of Containerization.
  • Guided Hands-On: Deploy a Ready-Made AI Model as a Usable Service.

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    Topics Covered

  • Understand Large Language Models (LLMs) and Their Applications.
  • Learn Retrieval-Augmented Generation (RAG), Grounding, and Prompt Engineering.
  • Understand the Difference Between Knowledge Updates and Information Retrieval.
  • Explore AI Agents and Methods for Evaluating AI Responses.
  • Guided Hands-On: Build a Document-Grounded AI Question-Answering Assistant.

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    Topics Covered

  • Understand the AI Model Deployment Process from Development to Production.
  • Learn About Response Time, Performance, and Deployment Trade-Offs.
  • Explore Safe Model Rollouts and Version Management.
  • Understand AI Deployment on Resource-Constrained Devices.
  • Guided Hands-On: Deploy a Model and Compare Multiple Model Versions.

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    Topics Covered

  • Understand the Role of Cloud Computing in AI.
  • Learn Why GPUs Power Modern AI Workloads.
  • Explore Containers, Orchestration, and Infrastructure as Code.
  • Understand Auto-Scaling for AI Applications.
  • Guided Hands-On: Observe AI Deployment and Auto-Scaling in a Cloud Environment.

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    Topics Covered

  • Understand the Importance of Post-Deployment AI Operations.
  • Learn AI Monitoring and Model Drift Detection.
  • Explore Troubleshooting and Maintenance of Production AI Systems.
  • Understand Continuous Integration and Continuous Deployment (CI/CD) Concepts.

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    Topics Covered

  • Understand AI Security Risks, Misuse, and Adversarial Threats.
  • Explore Bias, Fairness, and Data Privacy Principles.
  • Learn Cost Optimization Strategies for AI Systems.
  • Guided Hands-On: Review an AI Assistant for Security, Reliability, and Ethical Risks Using a Structured Evaluation Framework.

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    Topics Covered

  • Project 1: Build an AI Knowledge Assistant Grounded in Real Documents Using Instructor-Guided Templates.
  • Project 2: Deploy an AI Model as a Live Service Through a Guided End-to-End Deployment Workflow.
  • Project 3: Build a Simple AI Agent That Can Perform Actions Beyond Text Responses Using Pre-Built Notebooks and Templates.

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    AI Engineering & Infrastructure | IIT Kanpur