MLOps & LLM Ops

MLOps and LLM Ops are essential to ensuring that models are used effectively and efficiently. MLOps are responsible for managing the lifecycle of machine learning models in production. LLM Ops is the set of practices for managing the lifecycle of natural language processing models in production.

📕 MLOps books:

➡ Machine Learning Engineering with Python - Second Edition by Andy McMahon published by Packt
➡ Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen, MA, CPCC, Niall Murphy, Kranti K. Parisa, D. Sculley, Todd Underwood

📰 MLOps and LLM(Ops) blogs:

➡ Chip Huyen: https://huyenchip.com/blog/
➡ Eugene Yan: https://eugeneyan.com/writing/
➡ Decoding ML by Paul Iusztin: https://pauliusztin.substack.com/
➡ LLM watch by Pascal Biese: https://xaiguy.substack.com/

📽 LLM(Ops) courses:

➡ Hands-on LLMs by Pau Labarta Bajo, Alexandru Răzvanț, and Paul Iusztin: https://github.com/iusztinpaul/hands-on-llms
➡ LLM BootCamp by Sergey Karayev, Charles Frye, and Josh Tobin: https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/
➡ Introduction to Q&A Systems with LLMs by Rahul Parundekar and MLOps Community: https://learn.mlops.community/courses/llms/introduction-to-uage-models-llms/

🔖 Curated lists of references for MLOps/LLM(Ops):

➡ Awesome LLMOps by Ce Gao: https://github.com/tensorchord/Awesome-LLMOps
➡ Awesome MLOps by Dr. Larysa Visengeriyeva: https://github.com/visenger/awesome-mlops
➡ LLM roadmaps by Maxime Labonne: https://github.com/mlabonne/llm-course

💡Together with Başak and Raphaël, we run Marvelous MLOps, where we share MLOps cheatsheets, memes, and articles on MLOps.

💡Check out other recommendations on learning resources:

📕Books:

➡ Effective Data Science Infrastructure by Ville Tuulos, creator of Metaflow
➡ Engineering MLOps by Emmanuel Raj
➡ Machine Learning Engineering in Action by Benjamin Wilson

💻 Courses and tutorials:

#machinelearning #datascience