Kyryl Opens ML

This mini-course, "Serving Basics," is the fifth module of the comprehensive "Machine Learning in Production" course. While the full course covers all aspects of deploying and managing ML projects end-to-end, this mini-course focuses specifically on the deployment and serving of ML models or LLMs. Mastering serving is crucial for making your models available and performant in production environments, making it an essential component for anyone aiming to operationalize ML models effectively.

This mini-course is ideal for software engineers, ML professionals, tech switchers, and tech entrepreneurs interested in mastering the deployment and serving of ML models or LLMs using custom web servers and inference servers.

By the end of this course, you'll have a solid foundation in deploying and serving ML models using techniques such as pre-serving, custom web servers, and inference servers like TensorFlow Serving and TorchServe. You will also gain knowledge in serving platforms, patterns, and specific considerations for serving LLMs.

A basic understanding of programming concepts and familiarity with machine learning principles are recommended to get the most out of this course.

The course is designed to be completed in approximately 1.5 weeks. However, you can pace yourself according to your schedule and learning preferences.

No, you have access to the course materials indefinitely.

The course can be completed in about 8 hours total. You can choose to spread this out over a week or complete it in a single session, depending on your availability and learning style.

Don't worry! Our course comes with a supportive Discord community channel dedicated to homework help. Here, you can ask questions, seek guidance, and collaborate with fellow learners and the instructor. Additionally, for each practice task, we provide an example implementation to guide you through the process.

Yes, upon completing this course, you will receive a 70$ discount on the full "Machine Learning in Production" course. Use your email address as the promo code at checkout.