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MLOps Primer - 2021
A collection of resources to learn about MLOps.
Machine learning operations (MLOps) is becoming an exciting space as we figure out the best practices and technologies to deploy machine learning models in the real world. MLOps enable ML teams to build responsible and scalable machine learning systems and infrastructure. This facilitates tasks that range from risk assessment to building and testing to monitoring.
While still in its infancy, MLOps has attracted machine learning engineers and software engineers in general.
With every new paradigm comes new challenges and opportunities to learn. In this post, I highlight a few of the resources I am using to upskill and inform myself on the latest in the world of MLOps. I have listed a few educational resources as a start but I plan to build this out as a more comprehensive primer for the future. The primer will include emerging tools for MLOps, research, use cases, and best practices focusing on technical details while still making it accessible to every ML researcher and practitioner regardless of the level of experience.
I think the best place to get a high-level introduction of the MLOps space is in the book “Introducing MLOps” by Mark Treveil and the Dataiku team.
“MLOps Tooling Landscape” is a great blog post by Chip Huyen summarizing all the latest technologies used in MLOps.
Community & Resources
There are several efforts to keep the community informed on the latest development in the MLOps landscape. Here are a few popular ones:
🐙 Awesome MLOps - a collection of links and resources for MLOps
👩🏼💻 Machine Learning Ops - a collection of resources on how to facilitate Machine Learning Ops with GitHub.
🚀 MLOps course by Goku Mohandas - a series of lessons teaching how to apply ML to build production-grade products.
🎓 Machine Learning Engineering for Production (MLOps) Specialization - a new specialization by deeplearning.ai on machine learning engineering for production (MLOPs)
💬 MLOps Community - A place to have discussions about MLOps.
Below are some other references I found interesting while searching for articles on the topic of MLOps and ML engineering:
🎓 Improving software engineering skills as a data scientist
🎓 A chat with Andrew Ng on MLOps
A recent talk on MLOps by Andrew Ng focuses on the discussion of moving from model-centric approaches to data-centric approaches for machine learning.
Note that this is a work in progress. I am currently conducting some ongoing research in this space and will convert this blog post into a comprehensive primer for MLOPs. Until next time!
I try to regularly maintain this guide. To get regular updates on new ML and NLP resources, follow me on Twitter.