AI Engineering : Model Deployment, MLOps & Agentic AI

About this course

AI Engineering is one of the fastest-growing fields in technology today.

From deploying Machine Learning models and building scalable AI APIs to developing LLM-powered applications, enterprise AI systems, and autonomous AI Agents, organizations across every industry are looking for engineers who can transform AI models into production-ready solutions.

In this course, you'll learn how to build, deploy, manage, and serve Machine Learning, Deep Learning, and AI applications using modern tools and industry best practices. Going beyond model development, you'll gain hands-on experience with the complete AI Engineering lifecycle—from experiment tracking and model management to deploying production-ready REST APIs and AI Agents.

You'll also learn how enterprise AI teams use Databricks for experiment tracking, model management, model versioning, and managed model serving, giving you practical experience with one of the world's leading AI and data platforms.

The course introduces modern AI Engineering concepts including Large Language Models (LLMs), OpenAI SDK, OpenAI Agents SDK, Google ADK, AI Agents, MLOps, Databricks, MLflow, GitHub Copilot, Claude Code, Vibe Coding, and other emerging technologies shaping the future of software development.

Course Structure

Machine Learning Model Deployment

Build Classification Models using Scikit-learn

Save and Load Machine Learning ModelsExport Models across Environments

Build REST APIs using Python FlaskDeploy Machine Learning APIs on Cloud Virtual Machines

Build Serverless Machine Learning APIs using Cloud Functions

Deep Learning Model Deployment

Build and Deploy TensorFlow and Keras ModelsDeploy PyTorch ModelsConvert PyTorch Models using ONNX

Build REST APIs for

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