A guide and code-driven case study on MLOps for software engineers, data scientists and product managers. We will be
developing and deploying Made With ML's first ML service, from Product → ML → Production, with open source
What is it?
Ironically, Made With ML isn’t made with any ML yet (always go manual before ML) but now we have the data (3K quality projects) to apply ML and automate the tedious bits.
This ML service will act as a foundation for all future ML features and subsequent iterations.
The first feature is tagifai - multilabel classification of tags for a project.
We'll discuss the need and utility of this feature in the first lesson.
Who is this course for?
- ML developers looking to become end-to-end ML developers.
- Software engineers looking to learn how to responsibly deploy and monitor ML systems.
- Product managers who want to have a comprehensive understanding of the different stages of ML dev.
Click on each lesson to see the topics that will be covered or click here to toggle all lessons.
- Solutions (UX, technical)
- Constraints (performance, compute)
- Exploratory data analysis
- Experiment tracking
- Monitoring (performance, drift)
- CI/CD (GitHub actions)
- Active learning
- Lessons will be released weekly and will entail a
YouTube video and an article.
- Every lesson will include:
- Intuition: high level overview of the concepts that will be covered and how it all fits together.
- Code: simple code examples to illustrate the concept.
- Application: applying the concept to our specific task.
- Extensions: brief look at other tools and techniques that will be useful for difference situations.
What makes this course unique?
- Hands-on: If you search production ML or MLOps online, you'll find great
blog posts and tweets. But in order to really understand these concepts, you need to implement them.
Unfortunately, you don’t see a lot of the inner workings of running production ML because of scale, proprietary
content & expensive
tools. However, Made With ML is free, open and live which makes it a perfect learning opportunity for the community.
- Intuition-first: We will never jump straight to code. In every lesson, we
will develop intuition for the concepts and think about it from a product perspective.
- Software engineering: This course isn't just about ML. In fact, it's mostly about
clean software engineering! We'll cover important concepts like versioning, testing, logging, etc. that
really makes this a production-grade product.
- Focused yet holistic: For every concept, we'll not only cover what's most important
for our specific task (this is the case study aspect) but we'll also cover related methods (this is the guide
which may prove to be useful in other situations. For example, when we're serving our application, we'll expose our
latest model as an API endpoint. However, there are several other popular ways to serving models and we'll briefly
illustrate those and talk about advantages / disadvantages.
- Open source tools: We will be using only open source tools for this project, with the
exception of Google Cloud Platform for storage and compute (free credit will be plenty). The reason we're
constraining to open source tools is because:
We can focus on the fundamentals, everyone can do it and you will have much better
understanding when you do use a paid tool at work (if you want to).
- Large companies that deploy ML to production have complicated and scaled processes that don’t make sense for
vast majority of companies / individuals.