Introducing MLOps: How to Scale Machine Learning in the Enterprise

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do. This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach-Build, Manage, Deploy and Integrate, and Monitor-for creating ML-infused applications within your organization. You'll learn how to: Fulfill data science value by reducing friction throughout ML pipelines and workflows Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action

About the Author :
Mark Treveil has designed products in fields as diverse as telecoms, banking, and online trading. His own startup led a revolution in governance in the UK local government, where it still dominates. He is now part of the Dataiku Product Team based in Paris. Nicolas Omont is VP of operations at Artelys where he is developing mathematical optimization solutions for energy and transport. He previously held the role of Dataiku Product Manager for ML and advanced analytics. He holds a PhD in Computer Science, and he's been working in operations research and statistics for the past 15 years, mainly in the telecommunications and energy utility sectors. Clément Stenac is a passionate software engineer, CTO and co-founder at Dataiku. He oversees the design, development of the Dataiku DSS Entreprise AI Platform. Clément was previously head of product development at Exalead, leading the design and implementation of web-scale search engine software. He also has extensive experience with open source software, as a former developer of the VideoLAN (VLC) and Debian projects. Kenji Lefevre is VP Product at Dataiku. He oversees the product roadmap and the user experience of the Dataiku DSS Entreprise AI Platform. He holds a PhD in pure mathematics from University of Paris VII, and he directed documentary movies before switching to Data Science and product management. Du Phan is a Machine Learning engineer at Dataiku, where he works in democratizing data science. In the past few years, he has been dealing with a variety of data problems, from geospatial analysis to deep learning. His work now focuses on different facets and challenges of MLOps. Joachim Zentici is an Engineering Director at Dataiku. Joachim graduated in applied mathematics from Ecole Centrale Paris. Prior to joining Dataiku in 2014, he was a Research Engineer in computer vision at Siemens Molecular Imaging and INRIA. He has also been a teacher and a lecturer. At Dataiku, Joachim had multiple contributions including managing the engineers in charge of the core infrastructure, building the team for the plugins & ecosystem effort as well as leading the global technology training program for customer-facing engineers. Adrien Lavoillotte is Engineering Director at Dataiku where he leads the team responsible for machine learning and statistics features in the software. He studied at ECE Paris, a graduate school of engineering, and worked for several startups before joining Dataiku in 2015. Makoto Miyazaki is a Data Scientist at Dataiku and responsible for delivering hands-on consulting services using Dataiku DSS for European and Japanese clients. Makoto holds a Bachelor's degree in economics and a Master's Degree in data science, and he was also a former financial journalist with a wide range of beats, including nuclear energy and economic recoveries from the tsunami. Lynn Heidmann received her Bachelor of Arts in Journalism/Mass Communications and Anthropology from the University of Wisconsin-Madison in 2008 and decided to bring her passion for research and writing into the world of tech. She spent seven years in the San Francisco Bay Area writing and running operations with Google and subsequently Niantic before moving to Paris to head content initiatives at Dataiku. In her current role, Lynn follows and writes about technological trends and developments in the world of data and AI.