How to: MLOps Books
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1. [“Machine Learning Engineering” by Andriy Burkov, 2020](http://www.mlebook.com/wiki/doku.php?id=start)
1. ["ML Ops: Operationalizing Data Science" by David Sweenor, Steven Hillion, Dan Rope, Dev Kannabiran, Thomas Hill, Michael O'Connell](https://learning.oreilly.com/library/view/ml-ops-operationalizing/9781492074663/)
1. ["Building Machine Learning Powered Applications" by Emmanuel Ameisen](https://learning.oreilly.com/library/view/building-machine-learning/9781492045106/)
1. ["Building Machine Learning Pipelines" by Hannes Hapke, Catherine Nelson, 2020, O’Reilly](https://learning.oreilly.com/library/view/building-machine-learning/9781492053187/) 
1. ["Managing Data Science" by Kirill Dubovikov](https://www.packtpub.com/eu/data/managing-data-science)
1. ["Accelerated DevOps with AI, ML & RPA: Non-Programmer's Guide to AIOPS & MLOPS" by Stephen Fleming](https://www.amazon.com/Accelerated-DevOps-AI-RPA-Non-Programmers-ebook/dp/B07ZMJCJRS)
1. ["Evaluating Machine Learning Models" by Alice Zheng](https://learning.oreilly.com/library/view/evaluating-machine-learning/9781492048756/)
1. [Agile AI. 2020. By Carlo Appugliese, Paco Nathan, William S. Roberts. O'Reilly Media, Inc.](https://learning.oreilly.com/library/view/agile-ai/9781492074984/)
1. ["Machine Learning Logistics". 2017. By T. Dunning et al. O'Reilly Media Inc.](https://mapr.com/ebook/machine-learning-logistics/)
1. ["Machine Learning Design Patterns" by Valliappa Lakshmanan, Sara Robinson, Michael Munn. O'Reilly 2020](https://learning.oreilly.com/library/view/machine-learning-design/9781098115777/)
1. ["Serving Machine Learning Models: A Guide to Architecture, Stream Processing Engines, and Frameworks" by Boris Lublinsky, O'Reilly Media, Inc. 2017](https://www.lightbend.com/ebooks/machine-learning-guide-architecture-stream-processing-frameworks-oreilly)
1. ["Kubeflow for Machine Learning" by Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, Boris Lublinsky](https://learning.oreilly.com/library/view/kubeflow-for-machine/9781492050117/)
1. ["Clean Machine Learning Code" by Moussa Taifi. Leanpub. 2020](https://leanpub.com/cleanmachinelearningcode)
1. [E-Book "Practical MLOps. How to Get Ready for Production Models"](https://valohai.com/mlops-ebook/)
1. ["Introducing MLOps" by Mark Treveil, et al. O'Reilly Media, Inc. 2020](https://learning.oreilly.com/library/view/introducing-mlops/9781492083283/)
1. ["Machine Learning for Data Streams with Practical Examples in MOA", Bifet, Albert and Gavald\`a, Ricard and Holmes, Geoff and Pfahringer, Bernhard, MIT Press, 2018](https://moa.cms.waikato.ac.nz/book/)
1. ["Machine Learning Product Manual" by Laszlo Sragner, Chris Kelly](https://machinelearningproductmanual.com/)
1. ["Data Science Bootstrap Notes" by Eric J. Ma](https://ericmjl.github.io/data-science-bootstrap-notes/)
1. ["Data Teams" by Jesse Anderson, 2020](https://www.datateams.io/)
1. ["Data Science on AWS" by Chris Fregly, Antje Barth, 2021](https://learning.oreilly.com/library/view/data-science-on/9781492079385/)
1. [“Engineering MLOps” by Emmanuel Raj, 2021](https://www.packtpub.com/product/engineering-mlops/9781800562882)
1. [Machine Learning Engineering in Action](https://www.manning.com/books/machine-learning-engineering-in-action)
1. [Practical MLOps](https://learning.oreilly.com/library/view/practical-mlops/9781098103002/)
1. ["Effective Data Science Infrastructure" by Ville Tuulos, 2021](https://www.manning.com/books/effective-data-science-infrastructure)
1. [AI and Machine Learning for On-Device Development, 2021, By Laurence Moroney. O'Reilly](https://learning.oreilly.com/library/view/ai-and-machine/9781098101732/)
1. [Designing Machine Learning Systems ,2022 by Chip Huyen , O'Reilly ](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)
1. [Reliable Machine Learning. 2022. By Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood. O'Reilly](https://learning.oreilly.com/library/view/reliable-machine-learning/9781098106218/)
1. [MLOps Lifecycle Toolkit. 2023. By Dayne Sorvisto. Apress](https://link.springer.com/book/10.1007/978-1-4842-9642-4)
1. [Implementing MLOps in the Enterprise. 2023. By Yaron Haviv, Noah Gift. O'Reilly](https://www.oreilly.com/library/view/implementing-mlops-in/9781098136574/)