Made With ML

Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production ML applications.

While the specific task in this course involves fine-tuning an LLM for a supervised task, everything we learn easily extends to all applications (NLP, CV, time-series, etc.), models (regression → LLMs), data modalities (tabular, text, etc.), cloud platforms (AWS, GCP) and scale (local laptop → distributed cluster).

• Before we jump straight into the code, we develop a first principles understanding for every machine learning concept.
• Implement software engineering best practices as we develop and deploy our machine learning models.
• Easily scale ML workloads (data, train, tune, serve) in Python without having to learn completely new languages.
• Connect MLOps components (tracking, testing, serving, orchestration, etc.) as we build an end-to-end machine learning system.
• Learn how to quickly and reliably go from development to production without any changes to our code or infra management.
• Learn how to create mature CI/CD workflows to continuously train and deploy better models in a modular way that integrates with any stack.

Who is this course for?

Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person.

• All developers: Whether software/infra engineer or data scientist, ML is increasingly becoming a key part of the products that you'll be developing.
• College graduates: Learn the practical skills required for industry and bridge gap between the university curriculum and what industry expects.
• Product / Leadership: who want to develop a technical foundation so that they can build amazing (and reliable) products powered by machine learning.

You should know how to code in Python and the basics of machine learning.