Production Engineering from DevOps to MLOps (Production Engineering from DevOps to MLOps)
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Production Engineering from DevOps to MLOps

About the Book

This book takes a DevOps approach to MLOps and uniquely positions how MLOps is an extension of well-established DevOps principles using real-world use cases. It leverages multiple DevOps concepts and methodologies such as CI/CD and software testing. It also demonstrates the additional concepts from MLOps such as continuous training that expands CI/CD/CT to build, operationalize and monitor ML models. 

About the Authors

Sebastien Donadio
Sebastien Donadio

Sebastien has nearly two decades of experience in the fields of high-performance computing, software design and development, and financial computing. Currently an architect in Bloomberg Office of the CTO, he has a wide variety of professional experience, including serving as CTO of a FX/Crypto trading shop, head of software engineering at HC Technologies, quantitative trading strategy software developer at Sun Trading, partner at high-frequency trading hedge fund AienTech, and a technological leader in creating operating systems for the Department of Defense. He also has research experience with Bull, and as an IT Credit Risk Manager with Société Générale in France. Sebastien has taught various computer science and financial engineering courses over the past fifteen years at a variety of academic institutions, including the University of Versailles, Columbia University’s Fu Foundation School of Engineering and Applied Science, University of Chicago and NYU Tandon School of Engineering. Courses taught include: Computer Architecture, Parallel Architecture, Operating Systems, Machine Learning, Advanced Programming, Real-time Smart Systems, Computing for Finance in Python, and Advanced Computing for Finance. Sebastien holds a Ph.D. in High Performance Computing Optimization, an MBA in Finance and Management, and an MSc in Analytics from the University of Chicago. His main passion is technology, but he is also a scuba diving instructor and an experienced rock-climber.

Arnab Bose
Arnab Bose

Arnab Bose is Chief Scientific Officer at Abzooba, a data analytics company and Clinical Associate Professor at the University of Chicago teaching Advanced Linear Algebra, Machine Learning, Time Series Forecasting, MLOps and Health Analytics. He is a twenty-five year plus industry veteran focused on machine learning and deep learning models for unstructured and structured data. Arnab has extensive industry experience in using data to influence behavioral outcomes in healthcare, retail, finance, and automated vehicle control.

Arnab is an avid writer and has published numerous papers for conferences and academic journals, as well as book chapters. He enjoys public speaking and has given talks on data analytics at universities and industry conferences in the US, Australia, and India. He serves on the board of the financial engineering graduate program at University of Southern California. Arnab holds MS and PhD degrees in electrical engineering from University of Southern California and a BS in electrical engineering from the Indian Institute of Technology at Kharagpur, India. Arnab enjoys music, sports and runs in half-marathons.

Table of Contents

Chapter 1: Introducing DevOps, gives an overview of what DevOps is. It will introduce what an operating system is and how we can operate it. Chapter 2: Understanding clouding for devops, introduces how we can use the cloud in the DevOps approach. Chapter 3: Building software by understanding the whole toolchain, covers how to build software and how to enrich it with libraries. Chapter 4: Introducing MLOps, gets an insight into the challenges in operationalizing Machine Learning models. Chapter 5: Preparing the Data, gives insight into the importance of data in Machine Learning models. Chapter 6: Using a Feature Store, explains how a feature store promotes reusability to develop quick robust Machine Learning models. Chapter 7: Building ML models, describes how to manage a Machine Learning model. Chapter 8 Understanding ML Pipelines, depicts the importance of data feedback loops. Chapter 9. Interpreting and Explaining ML models, discusses how to dissect an ML model from the perspectives of explainability and interpretability. Chapter 10 Building Containers and Managing Orchestration, presents the use of containerized applications. Chapter 11 Testing ML Models, describes how testing improves software quality; it is a critical part of the DevOps/MLOps process. Chapter 12 Monitoring ML Models, illustrates what are the different ways that an ML model can underperform in production. Chapter 13 Evaluating Fairness, explain what bias and fairness are in ML models. Chapter 14 Exploring Antifragility and ML Model Environmental Impact, comprehends antifragility and how it can be used to make your ML model robust and the environment impact of your ML model.

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