Implementing MLOps in the Enterprise: A Production-First ApproachКНИГИ » ПРОГРАММИНГ
Название: Implementing MLOps in the Enterprise: A Production-First Approach Автор: Yaron Haviv, Noah Gift Издательство: O’Reilly Media, Inc. Год: 2024 Страниц: 377 Язык: английский Формат: epub (true) Размер: 15.5 MB
With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.
Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements.
Deep Learning (DL) is a Machine Learning subdomain inspired by the human brain’s structure and functioning. In Deep Learning, neural networks consisting of interconnected layers of artificial neurons process data hierarchically and can capture complex patterns in data. Each layer learns and transforms the input data, gradually capturing higher-level features and abstractions. The DL training process involves feeding labeled data to the neural network and adjusting the weights and biases of the neurons iteratively. It can reduce the dependency on manual feature engineering and achieve impressive results in various domains such as computer vision, natural language processing, speech recognition, and reinforcement learning. DL technologies are transforming the world with innovations such as transformers, generative AI, ChatGPT, and more.
This book will help you: • Learn the MLOps process, including its technological and business value • Build and structure effective MLOps pipelines • Efficiently scale MLOps across your organization • Explore common MLOps use cases • Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI • Learn how to prepare for and adapt to the future of MLOps • Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
Who This Book Is For: This book is for practitioners in charge of building, managing, maintaining, and operationalizing the Data Science process end to end: the heads of Data Science, heads of ML engineering, senior data scientists, MLOps engineers, and Machine Learning engineers. These practitioners are familiar with the nooks and crannies (as well as the challenges and obstacles) of the Data cience pipeline, and they have the initial technological know-how, for example, in Python, Pandas, Sklearn, and others.
This book can also be valuable for other technology leaders like CIOs, CTOs, and CDOs who want to efficiently scale the use of AI across their organization, create AI applications for multiple business use cases, and bridge organizational and technological silos that prevent them from doing so today.
The book is meant to be read in three ways. First, in one go, as a strategic guide that opens horizons to new MLOps ideas. Second, when making any strategic changes to the pipeline that require consultation and assistance. For example, when introducing real-time data into the pipeline, scaling the existing pipeline to a new data source/business use case, automating the MLOps pipeline, implementing a Feature Store, or introducing a new tool into the pipeline. Finally, the book can be referred to daily when running and implementing MLOps. For example, for identifying and fixing a bottleneck in the pipeline, pipeline monitoring, and managing inference.
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