What you'll learn:
- You will Learn to Build a Complete Scalable Software Application Which Is Powered By a Large Language Model And Deploy It At Scale on Amazon Web Services
- You Will learn to Integrate Your Application's LLM Powered Backend with Streamlit UI Frontend
- You Will First Learn To Locally Test Your Application , Then Package It Using Docker And Finally Learn The Best Practices For Using Streamlit Inside Docker
- You Will Learn a Template & Best Practices to Inject your OpenAI's APIÂ Keys Into Your Containerized Application At Run Time
- You Will Learn To Address Vulnerabilities In Your Containerized Application And Best Practices To Resolve Them
- You Will Learn to Design Your System's Architecture Based On The Components And Design Choices In Your Application
- You Will Learn the Differences Between Horizontal Scaling and Vertical Scaling
- You Will Learn in Depth to Apply Serverless Deployment and Learn To apply Load Balancers and Auto Scaling To Your Application
- You Will Be Able To Apply Your Learnings To Build Deploy & Scale other LLM Powered Langchain Applications
Are you ready to dive deep into the world of Machine Learning Engineering and build powerful software applications? Our Machine Learning Engineering course is designed to equip you with the skills and knowledge to harness the full potential of Langchain, integrate the OpenAI API, deploy applications on AWS Elastic Container Service, and efficiently manage scaling using Load Balancers and Auto Scaling Groups.
In this hands-on course, you'll learn how to create robust ML applications from the ground up. We'll start by mastering Langchain, a cutting-edge language model, and demonstrate how to seamlessly inject your OpenAI API key into the prediction pipeline at runtime. You'll gain proficiency in designing and developing ML applications that can understand, process, and generate human-like text.
As you progress, we'll explore the fundamental concepts of Horizontal Scaling and Vertical Scaling, providing a clear understanding of when and how to implement each strategy. You'll then discover how to scale your ML application with ease by deploying Application Load Balancers and Auto Scaling Groups on AWS, ensuring high availability and fault tolerance.
By the end of this course, you'll be well-versed in building ML-driven software applications, deploying them on AWS, and scaling them to meet the demands of your users. Join us on this exciting journey into the world of Machine Learning Engineering and become a skilled practitioner in this rapidly evolving field.
