Machine Learning Governance for Managers
Springer International Publishing (Verlag)
978-3-031-31804-7 (ISBN)
Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance.
Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized.Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide.
Francesca Lazzeri, Ph.D. is an experienced data and machine learning scientist with over fifteen years of academic research, tech industry and engineering team building/management experience. Francesca is Professor of machine learning at Columbia University and Principal Data Scientist Manager at Microsoft, where she leads an organization of data scientists and machine learning engineers building data science and machine learning applications. Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit.
Alexei Robsky has more than twelve years of experience building tech products, leading engineering and data science teams, and driving growth by applying data science solutions to business problems. Alexei is a Data Science Manager at Twitter, supporting a data science organization that focuses on Personalization and User experience. Prior to his role at Twitter, Alexei was a Principal Data Science Manager at Microsoft, where he led data scientists, machine learning engineers, and data engineers deploying production-level solutions to drive Microsoft Azure customer experience. Alexei holds an MBA from Duke University and BSc. in Electrical Engineering and Computer Science from Tel Aviv University.
1. Understanding Business Goals.- 2. Measure the Right Things.- 3. Searching for the Right Tools.- 4. MLOps Governance & Architecting the Data Science Solution.- 5. Unifying Organizations' Machine Learning Vision.
| Erscheinungsdatum | 25.11.2023 |
|---|---|
| Zusatzinfo | XIX, 108 p. 17 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 207 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Data Science Function and Management • Data Science Lifecycle • Data Science Operations • Machine Learning Governance • Machine Learning Operations • MLOps |
| ISBN-10 | 3-031-31804-8 / 3031318048 |
| ISBN-13 | 978-3-031-31804-7 / 9783031318047 |
| Zustand | Neuware |
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