Want To Learn MLOps?
Tags: machinelearning, operations, dataengineering, mlops
Published: 2021-08-16 06:44:50

**MLOps** is not a piece of cake. Especially in today’s changing environment. There are many challenges—construction, integrating, testing, releasing, deployment, and infrastructure management. You need to follow good practices and know-how to adjust to the challenges.

Being an emerging field, MLOps is rapidly gaining momentum amongst Data Scientists, ML Engineers, and AI enthusiasts. Following this trend, the Continuous Delivery Foundation SIG MLOps differentiates the ML models management from traditional software engineering and suggests the following MLOps capabilities:

MLOps aims to unify the release cycle for machine learning and software application release.

MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)

MLOps enables the application of agile principles to machine learning projects.

MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.

MLOps reduces technical debt across machine learning models.

MLOps must be a language-, framework-, platform-, and infrastructure-agnostic practice.

And if you don’t learn and develop your knowledge, you’ll fall out of the loop. The right resources can help you follow the best practices, discover helpful tips, and learn about the latest trends.

You don’t have to look far. Here’s your list of the best go-to resources about MLOps—books, articles, podcasts, and more. 

*Let’s dive in!*

[1] [Introducing MLOps](https://www.oreilly.com/library/view/introducing-mlops/9781492083283/) from O’Reilly

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/itygwd2ccvvi4s6g32sa.png)

Introducing MLOps: How to Scale Machine Learning in the Enterprise is a book written by Mark Treveil and the Dataiku Team (collective authors). It introduces the key concepts of MLOps, shows how to maintain and improve ML models over time, and tackles the challenges of MLOps.

The book is divided into three parts:

An introduction to the topic of MLOps, how and why it has developed as a discipline, who needs to be involved to execute MLOps successfully, and what components are required.

The second part follows the machine learning model life cycle, with chapters on developing models, preparing for production, deploying to production, monitoring, and governance.

Provides tangible examples of how MLOps looks in companies today, so readers can understand the setup and implications in practice.

-------------------------------------------------------------
[2] [What Is MLOps?](https://www.oreilly.com/library/view/what-is-mlops/9781492093626/) from O’Reilly

![Alt Text](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tmkyrzpi5ejspftvvak7.png)

What Is MLOps? Generating Long-Term Value from Data Science & Machine Learning by Mark Treveil and Lynn Heidmann is a thorough report for business leaders who want to understand and learn about MLOps as a process for generating long-term value while reducing the risk associated with data science, ML, and AI projects.

Here’s what the report includes:

Detailed components of ML model building, including how business insights can provide value to the technical team

Monitoring and iteration steps in the AI project lifecycle–and the role business plays in both processes

How components of a modern AI governance strategy are intertwined with MLOps

Guidelines for aligning people, defining processes, and assembling the technology necessary to get started with MLOps.

-----------------------------------------------------------------

[3] [Google Cloud] (https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning) 

**MLOps: Continuous delivery and automation pipelines in machine learning** is a document from Google that “discusses techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems.”

If you’re new to MLOps, this document can be a great source of knowledge as it touches on some basic concepts. But if you’re the MLOps veteran, you’ll also find it helpful to refresh and solidify your knowledge. It can also help reliably build and operate ML systems at scale.

---------------------------------------------------------------
[4] Awesome MLOps and production machine learning GitHub lists

An [Awesome list](https://github.com/sindresorhus/awesome) is a thematic curated catalog of resources, hosted in the form of a GitHub repository containing only a README file.

In our case, two very useful lists are the [Awesome MLOps](https://github.com/visenger/awesome-mlops) and the [Awesome Production Machine Learning] (https://github.com/EthicalML/awesome-production-machine-learning). While the former focuses on learning resources, the latter complements it with an emphasis on tooling.

These lists are useful when you already have a comprehensive view of the MLOps field and you would like to specialize in a given subdomain, such as model serving and monitoring.

-------------------------------------------------------------

[5] [Stanford MLSys Seminar Series] (https://mlsys.stanford.edu/)

The Stanford MLSys Seminar Series is, as the name suggests, a series of seminars focused on machine learning and ML systems—tools and all the technology used for programming machine learning models.

--------------------------------------------------------------
[6] [Awesome MLOps] (https://github.com/visenger/awesome-mlops)

This is An awesome list of references for MLOps – Machine Learning Operations from ml-ops.org

It’s a list of links to numerous resources, beginning with books, articles, to communities, and many, many more. In a word—it has everything you could possibly read about MLOps. The table of contents includes among others: MLOps Papers, Talks About MLOps, Existing ML Systems, Machine Learning, Software Engineering Product Management for ML/AI, The Economics of ML/AI, Model Governance, Ethics, Responsible AI.

---------------------------------------------------------------

**References:**
[Towards Data Science] (https://towardsdatascience.com/what-is-mlops-everything-you-must-know-to-get-started-523f2d0b8bd8)

[Road To MLOps] (https://builtin.com/machine-learning/mlops)

[MLOps - neptune.ai] (https://neptune.ai/blog/category/mlops)

[ml-ops.org] (https://ml-ops.org/)

[ml-ops Books] (https://ml-ops.org/content/references.html)

[Full Stack Deep Learning ](https://fall2019.fullstackdeeplearning.com/)

[3-best-free-online-resources-to-learn-mlops] (https://towardsdatascience.com/the-3-best-free-online-resources-to-learn-mlops-54816904f485)

[Where Can You Learn About MLOps? What Are the Best Books, Articles, or Podcasts to Learn MLOps? - neptune.ai] (https://neptune.ai/blog/learn-mlops-books-articles-podcasts)

[Machine Learning Operations – MLOps | Microsoft Azure] (https://azure.microsoft.com/en-in/services/machine-learning/mlops/#resources)

[7 Best Resources To Learn MLOps In 2021](https://analyticsindiamag.com/7-best-resources-to-learn-mlops-in-2021/)

[@SeattledataGuy] (https://dev.to/seattledataguy/mlops-and-machine-learning-roadmap-o7p)