Some notes on how people are defining MLOps and what general principles we can draw from that.

• O’Reilly book by Ted Dunning and Ellen Friedman
• The book is free, because they are hoping it will serve as good marketing material for MapR Convergent Data Platform.
• Focuses on the larger issue of “Machine Learning is more than just developing a model and getting it into production.”
• Talks about the need for a holistic approach to the cross-cutting concerns of ML that we will call “machine learning logistics” and “model management.”
• Talks about the whole system from start to finish
• The book may be a bit dated, as it was published in 2017.
• The book itself provides useful content. It’s not just marketing material. Every now and then, you see “tools like MapR Convergent Data Platform”, etc.

Chapter 02

• Logistics and model management need to be flexible to handle many different use cases and scenarios.
• But you don’t want it to be so complicated that it is also a hindrance to people getting work done.

Ingredients of the Rendezvous Approach

• “The rendezvous architecture takes advantage of data streams and geo-distributed stream replication to maintain a responsive and flexible way to collect and save data, including raw data, and to make data and multiple models available when and where needed.”
• “The design strongly supports ongoing model evaluation and multi-model comparison. It’s a new approach to managing models that reduces the burden of logistics while providing exceptional levels of monitoring so that you know what’s happening.”

Machine Learning Logistics Book

Chapter 1

Intro

• “Model management” is a term for the holistic view of the entire machine learning system.
• It’s not the cool part of Machine Learning, but it’s extremely important.
• New practitioners are going to need some practical advice on this matter.

Machine Learning Operations https://ml-ops.org/ This is a website featuring several articles that lay the foundation for the “what”, the “why”, and the “how” of machine learning operations, setting it apart as a discipline distinct from DevOps. Machine Learning operations maturity model https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model A rubric for evaluating the maturity of machine learning operations activities at your company. Books Title Review Edition Author Pub Date Publisher Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications 1st Edition Chip Huyen June 21, 2022 O’Reilly Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps 1st Edition Lakshmanan, Robinson, Munn November 10, 2020 O’Reilly MLOps Engineering at Scale 1st Edition Carl Osipov March 1, 2022 Manning Machine Learning Logistics: Model Management in the Real World 1st Edition Ted Dunning and Ellen Friedman August 23, 2017 O’Reilly Presentations Title Presenter Conference Date MLOps: The Most Important Piece in the Enterprise AI Puzzle Francesca Lazzeri QCon Plus 2022-05-06 Frameworks and Offerings CD Foundation SIG MLOps - MLOps Roadmap MLFlow Rendezvous Architecture CRISP-DM CRISP-ML Vetiver SageMaker Podcasts mlops.