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Overview: Understand how to use MLOps as an engineering discipline to help with the challenges of bringing Machine Learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate Machine Learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline’s aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book’s early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale Machine Learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. Once data scientists have access to the needed dataset or available features, they will start analyzing them and evaluating whether they are suitable for the ML task at hand. To perform medium- to large-scale data analysis, they will need access to compute resources beyond their laptop so those data crunching needs will be completed in a short amount of time. This is where distributed data computation engines come into the picture. Examples of these engines are Apache Spark, Dask, and Ray. Ray is a compute framework to enable efficient distributed execution of Python and AI workloads, boasting a simple programming model and automatic parallelization. For Machine learning practitioners, data scientists, and software engineers who are focusing on building Machine Learning systems and infrastructure to bring ML models to production.
Genre: Non-Fiction > Tech & Devices
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