Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain in-depth knowledge and hands-on experience with AI agents and MLOps, crucial components for developing and deploying production-ready AI solutions. You will begin by exploring various AI agents, including AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT. The course provides practical insights on how these frameworks can automate AI workflows and create autonomous AI agents. You will have the opportunity to implement these agents, developing AI-driven systems that can carry out tasks like decision-making, automation, and optimization. The second part of the course delves into MLOps, focusing on the operationalization of machine learning models. You’ll explore MLOps concepts such as versioning, automation, and monitoring, and how they fit into the broader context of machine learning deployment. Through hands-on exercises, you will learn to set up MLOps environments using tools like Git, Docker, and Kubernetes, and develop end-to-end machine learning pipelines. The course emphasizes the critical differences between experimentation and production in machine learning, teaching you how to build robust systems that can seamlessly move from development to deployment. The course also covers the necessary infrastructure for MLOps, including cloud platforms like AWS, GCP, and Azure, and how to containerize models using Docker. You will gain practical skills in deploying and managing machine learning models at scale using Kubernetes, ensuring your models are production-ready and scalable. This comprehensive journey will provide you with the tools to manage ML workflows, optimize deployment processes, and integrate AI agents into production environments. This course is designed for AI practitioners, data scientists, and engineers interested in taking their machine learning and AI systems to production. A basic understanding of machine learning concepts and programming is recommended, as the course focuses on applying these concepts in real-world production settings. Suitable for intermediate learners, this course provides both theoretical knowledge and practical experience in AI and MLOps. By the end of the course, you will be able to implement AI agents using advanced frameworks, set up MLOps pipelines, containerize and deploy models, and manage machine learning models in cloud and on-premise environments.

Syllabus

AI Agents – A Comprehensive Overview

In this module, we will provide a thorough overview of AI agents, exploring popular frameworks like AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT. You’ll engage in practical, hands-on exercises with each framework to understand how they enable the development of autonomous AI applications. This section equips you with the skills to automate and streamline AI workflows using cutting-edge technologies.

Introduction and Hands-On MLOps

In this module, we will dive deep into MLOps, covering essential concepts like versioning, automation, and monitoring, and how they apply to operationalizing machine learning models. You’ll explore the ML lifecycle from data preparation to production deployment, while gaining hands-on experience in setting up MLOps pipelines using Git, Docker, and Kubernetes. This section also focuses on using cloud platforms and infrastructure for efficient model orchestration and management.

Final Quiz and Congratulations

In this final section, we will congratulate you on completing the course and celebrate your achievements. You’ll take a quiz to assess your understanding of the course material and gain insights into your progress. We’ll also provide guidance for the next steps in your AI and MLOps journey, helping you continue to grow and apply your new skills in real-world applications.