{"product_id":"machine-learning-engineering-with-python-second-edition-manage-the-lifecycle-of-machine-learning-models-using-mlops-with-practical-examples-paperback","title":"Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples - Paperback","description":"\u003cp\u003eby \u003cb\u003eAndrew McMahon\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTransform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThis second edition delves deeper into key machine learning topics, CI\/CD, and system design\u003c\/li\u003e\n\u003cli\u003eExplore core MLOps practices, such as model management and performance monitoring\u003c\/li\u003e\n\u003cli\u003eBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eMachine Learning Engineering with Python, 2nd Edition, is the practical guide that MLOps and ML engineers need to build robust solutions to solve real-world problems, providing you with the skills and knowledge you need to stay ahead in this rapidly evolving field.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book takes a hands-on, examples-focused approach providing essential technical concepts, implementation patterns, and development methodologies. You'll go from understanding the key steps of the machine learning development lifecycle to building and deploying robust machine learning solutions. Once you've mastered the basics, you'll get hands-on with deployment architectures and discover methods for scaling up your solutions.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis edition goes deeper into ML engineering and MLOps, with a sharper focus on ML. You'll take CI\/CD further with continuous training and testing and go in-depth into data and concept drift.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eWith a new generative AI chapter, explore Hugging Face, PyTorch, and GitHub Copilot, and consume an LLM via an API using LangChain. You'll also cover deep learning considerations regarding workflow, hardware, and scaling up workloads, as well as orchestrating workflows with Airlfow and Kafka. And take advantage of ZenML as an open-source option for pipelining dataflows, and take deployment further with canary, blue, and green deployments.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePlan and manage stages of machine learning development projects\u003c\/li\u003e\n\u003cli\u003eExplore ANNs, DNNs, and LLMs, and get to grips with the rise of generative AI in MLOps\u003c\/li\u003e\n\u003cli\u003eUse Python to package your own ML tools and scale up solutions with Apache Spark, Kubernetes, and Apache Airflow\u003c\/li\u003e\n\u003cli\u003eUse AutoML for hyperparameter tuning\u003c\/li\u003e\n\u003cli\u003eDetect drift and build robust mechanisms into your solutions\u003c\/li\u003e\n\u003cli\u003eSupercharge your error handling with robust control flows and vulnerability scanning\u003c\/li\u003e\n\u003cli\u003eHost and build an ML microservice using AWS and Flask\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you're not a developer but want to manage or understand the product lifecycle of these systems, you'll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 462\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.93 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e August 31, 2023\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42704490201151,"sku":"9781837631964","price":71.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/c068565afc684c6e55c54264012cb0f0.webp?v=1765033430","url":"https:\/\/dhl-adrianne.myshopify.com\/products\/machine-learning-engineering-with-python-second-edition-manage-the-lifecycle-of-machine-learning-models-using-mlops-with-practical-examples-paperback","provider":"BBB","version":"1.0","type":"link"}