MLOps learning resources

Getting started with MLOps is not easy. In one of the previous posts, we shared the MLOps roadmap for 2024. In this post, we share resources that we recommend for MLOps and LLMOps.

Happy learning!

📕 Books

1. Designing Machine Learning Systems by Chip Huyen

2. Machine Learning Design Patterns by Sara Robinson, Valliappa Lakshmanan, Michael Munn

3. Introducing MLOps by Mark Treveil & the Dataiku team

4. Effective Data Science Infrastructure by Ville Tuulos

5. Engineering MLOps by Emmanuel Raj

6. Machine Learning Engineering with Python — Second Edition by Andy McMahon

7. Machine Learning Engineering in Action by Benjamin Wilson

8. Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen, MA, CPCC, Niall Murphy, Kranti K. Parisa, D. Sculley, Todd Underwood

📩 Newsletters and blogs

1. Decoding ML 𝖻𝗒 Paul Iusztin, Alexandru Razvant, Alex Vesa

2. The machine learning engineer by Alejandro Saucedo

3. ByteByteGo newsletter 𝖻𝗒 Alex Xu, Sahn Lam, Hua Li

4. Building ML Products 3.0 𝖻𝗒 👩🏻‍💻 Mikiko Bazeley 👘

5. Blog of Chip Huyen

6. Blog of Eugene Yan

🎙 Podcasts

1. MLOps community podcast by Demetrios Brinkmann

2. ML Platform Podcast by neptune.a

3. Let’s talk MLOps by Becky Gorringe 🦖

4. MLOps Weekly podcast 𝖻𝗒 Simba Khadder

5. Let’s talk AI by Thomas Bustos

👨‍💻👩‍💻 Courses and tutorials

1. MLOps Zoomcamp by DataTalksClub

2. Made with ML by Goku Mohandas

3. Taking Python to Production: A Professional Onboarding Guide by Eric Riddoch

4. Real World ML by Pau Labarta Bajo

5. The Full Stack 7-Steps MLOps Framework by Paul Iusztin

6. Hands-on LLMs by Pau Labarta Bajo, Alexandru Răzvanț, and Paul Iusztin

7. LLM BootCamp by Sergey Karayev, Charles Frye, and Josh Tobin

🤝 Community

1. MLOps community Slack channel

2. Discord channel of Chip Huyen

3. DataTalksClub Slack channel

🔖 Curated lists of references 

1. Awesome LLMOps by Ce Gao

2. Awesome MLOps by Dr. Larysa Visengeriyeva

3. LLM roadmaps by Maxime Labonne