MadeWithML's MLOps Fundamentals By Goku Mohandas
- kokilas203
- Aug 11, 2021
- 2 min read
All the machine learning fundamentals and MLOps lessons are released!
Check out this tweet for a quick summary of all the different topics we’ve covered.
Project-based
Intuition & application (code)
26K+ GitHub ️
30K+ community
47 lessons, 100% open-source

🛠️ Highly recommended industry resource.
❤️ 30K+ community members and growing. 🏆 Among the top MLOps repositories on GitHub.
Who is this course for?
Software engineers
Data scientists
College graduates
Product managers
MadeWithML's MLOps Course
All lessons released (100% open-source)
We finally completed all the MLOps lessons and we’ve covered everything from product → data → modeling → testing → reproducibility → monitoring and more to really close the loop.
With the help from the community, we’ve become:
among the top MLOps repositories on GitHub
highly recommended industry resource
But, what we’re most proud of is working with the community feedback to create unique content that follows these core principles:
1. Intuition-first We never jump straight to code, instead we develop an intuition for the concepts first. This allows us to extend and adapt our understanding of the concepts as this space matures.
2. Hands-on Instead of just discussing MLOps concepts at a high level, we actually code everything. Take a look at our testing or monitoring lessons to get a sense of the level of technical detail we dive into.
3. Engineering It’s not just about ML, it’s also about adhering to software engineering’s best practices. This includes basic scripting → API design → testing + more.
4. Comprehensive The lessons easily extend to all algorithms, data types (text, image, tabular), frameworks, cloud providers, etc. We avoid choosing any specific tech stack because so many real-world decisions are contextual. However we provide the foundation you need to be able to easily make those decisions and adapt to any current/future tools.
What's next?
All these lessons will always be 100% open-source and we’ll continue to keep them updated as this space matures. We’ve laid out the foundations needed and we have a lot more content to publish as practices become more widely adopted. We’ll also dive a whole lot deeper into many of the covered topics later this summer. Request: Our only ask is that you consider sharing the lessons with fellow coworkers, junior developers and especially new college grads who need to know this content in order to responsibly deliver value with ML.
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