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 primer, we highlight a few available resources to upskill and inform yourself on the latest in the world of MLOps. We have listed a few educational resources as a start but plan to build this out as a more comprehensive guide for the future.
Improving software engineering skills as a data scientist
🔗 https://ljvmiranda921.github.io/notebook/2020/11/15/data-science-swe/
MLOps Tooling Landscape - a great blog post by Chip Huyen summarizing all the latest technologies used in MLOps.
🔗 https://huyenchip.com/2020/12/30/mlops-v2.html
MLOps: From Model-centric to Data-centric AI - 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.
🔗 https://youtu.be/06-AZXmwHjo
Designing Machine Learning Systems (by Chip Huyen) - discusses a holistic approach to designing ML systems that focus on many important aspects of maintaining ML systems in production.
🔗 https://learning.oreilly.com/library/view/designing-machine-learning/9781098107956/
Introducing MLOps - One of the best places to get a high-level introduction of the MLOps space is in the book “Introducing MLOps” by Mark Treveil et al.
🔗 https://www.oreilly.com/library/view/introducing-mlops/9781492083283/
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
🔗 https://github.com/visenger/awesome-mlops
Machine Learning Ops - a collection of resources on how to facilitate Machine Learning Ops with GitHub.
🔗 https://mlops.githubapp.com/
MLOps Community - A place to have discussions about MLOps.
MLOps Zoomcamp - teaches practical aspects of productionizing ML services.
🔗 https://github.com/DataTalksClub/mlops-zoomcamp
Full Stack Deep Learning - this course shares best practices for the full stack; topics range from problem selection to dataset management to monitoring.
🔗 https://fullstackdeeplearning.com/
Machine Learning Engineering for Production (MLOps) Specialization - a new specialization by deeplearning.ai on machine learning engineering for production (MLOPs)
🔗 https://www.deeplearning.ai/program/machine-learning-engineering-for-production-mlops/
MLOps course (by Goku Mohandas) - a series of lessons teaching how to apply ML to build production-grade products.
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
A concise overview of MLOPs.
🔗 https://arxiv.org/abs/2205.02302
This collection is far from exhaustive but it should provide a good foundation to start learning about MLOPs. Reach out on Twitter if you have any questions.