AI Engineering : Model Deployment, MLOps & Agentic AI

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 Models

• Export Models across Environments

• Build REST APIs using Python Flask

• Deploy 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 Models

• Deploy PyTorch Models

• Convert PyTorch Models using ONNX

• Build REST APIs for TensorFlow and PyTorch Models

• Deploy Text Classification Models

• Deploy TensorFlow.js Models with JavaScript

Modern MLOps with MLflow

• Introduction to Modern MLOps

• Track Machine Learning Experiments with MLflow

• Compare Training Runs with MLflow

• Enable Automatic Experiment Logging

• Deploy Models using MLflow

• Understand Experiment Tracking, Model Registry, and the Machine Learning Lifecycle

Enterprise AI Engineering with Databricks

• Create and Navigate a Databricks Workspace

• Build and Track Machine Learning Models in Databricks

• Accelerate Development using the Built-in GenAI Assistant

• Track Experiments using Integrated MLflow

• Register and Version Models

• Deploy Managed Model Serving Endpoints

AI-Assisted Development with GitHub Copilot

• Agent Mode with GitHub Copilot

• Vibe Coding for Machine Learning

• Build REST APIs using GitHub Copilot

• Build Interactive Machine Learning Applications

• Build Serverless Machine Learning APIs using AWS

Generative AI and LLM Fundamentals

• OpenAI and GPT Models

• OpenAI Python SDK and Responses API

• Text Generation

• Image Generation

• Text-to-Speech

• Prompt Engineering

• Build AI Chatbots

Building AI Agents with the OpenAI Agents SDK

• Build Your First AI Agent

• Tool Calling

• Memory

• Multi-turn Conversations

• Web Search

• FastAPI Deployment

• Build Multi-Tool AI Agents

• Tracing with the OpenAI Agents SDK

• Build an AI Stock Alert Agent

• Build Multi-Agent AI Systems

Building AI Agents with Google ADK

• Introduction to Google ADK

• Set Up the Google ADK Development Environment

• Build AI Agents

• Add Tools to AI Agents

• Build Multi-Agent Applications

• Migrate OpenAI Agents SDK Projects to Google ADK

AI Agent Engineering Principles

• AI Agent Architecture Explained

• AI Agent Design Patterns

• Designing AI Agents Before Writing Code

• AI Agent Best Practices

The Road Ahead

• The Future of AI Agents

• Future-Proofing Your AI Career

Advanced Deep Learning & NLP Deployment

• Deep learning fundamentals

• PyTorch & TensorFlow model deployment

• REST APIs & TensorFlow Serving

• Docker & ONNX

• NLP with Bag-of-Words & TF-IDF

• Text classification

• Serverless deployment

Appendix: Specialized Deployment Techniques

• Machine learning models as code

• Model storage & retrieval

• PostgreSQL model registry

• TensorFlow.js fundamentals

• Browser-based AI applications

• Additional deployment techniques

This course is designed for beginners with no prior experience in Machine Learning or Deep Learning. A basic understanding of Python programming is recommended.

By the end of this course, you'll be able to build, deploy, manage, and serve Machine Learning models, Deep Learning models, LLM-powered applications, and AI Agents using Python, Databricks, MLflow, TensorFlow, PyTorch, FastAPI, OpenAI SDK, OpenAI Agents SDK, Google ADK, GitHub Copilot, Claude Code, and modern cloud deployment techniques.

As the AI landscape continues to evolve, new lectures and emerging technologies will be added regularly, ensuring this course remains a comprehensive and up-to-date resource for AI Engineering, Enterprise AI Engineering, Databricks, Model Deployment, MLOps, LLM Applications, and Agentic AI.