AI Engineering and Deployment

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 comprehensive course, you will explore the entire AI development lifecycle, from building machine learning models to deploying them in real-world environments. Starting with an introduction to TensorFlow, you’ll learn how to set up your development environment, create machine learning models, and understand the inner workings of neural networks. You’ll dive deep into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and learn how to leverage pre-trained models for transfer learning to improve model performance. As you progress, the course introduces you to cutting-edge topics like AI agents, where you will explore their role in industries ranging from healthcare to entertainment. You will learn how to build AI agents using frameworks such as AutoGPT, IBM Bee, and LangGraph. Moreover, you will gain practical skills in deploying AI models with TensorFlow Serving, TensorFlow Lite for mobile applications, and scale models using Kubernetes. The course also touches upon important ethical and legal considerations in AI development, making it a well-rounded introduction to real-world AI deployment. This course is ideal for learners with a basic understanding of machine learning and programming who want to take their skills to the next level. By the end of the course, you will be well-equipped to design, develop, deploy, and optimize AI models, as well as build autonomous AI agents for various applications. By the end of the course, you will be able to build and deploy complex AI models using TensorFlow, design AI agents with state-of-the-art frameworks, and address real-world challenges like scaling, ethical concerns, and regulatory issues in AI development.