{"product_id":"machine-learning-engineering-with-python-second-edition-manage-the-lifecycle-of-machine-learning-models-using-mlops-with-practical-examples-9781837631964","title":"Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples","description":"\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\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\u003eKey Features\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\u003eBook Description\u003cp\u003eThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.\u003c\/p\u003e\u003cp\u003eThe book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized \"model factory\" for training and retraining of models. You'll learn to employ concepts like CI\/CD and how to detect different types of drift.\u003c\/p\u003e\u003cp\u003eGet hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.\u003c\/p\u003e\u003cp\u003eWith a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003ePlan and manage end-to-end ML development projects\u003c\/li\u003e\n\u003cli\u003eExplore deep learning, LLMs, and LLMOps to leverage generative AI\u003c\/li\u003e\n\u003cli\u003eUse Python to package your ML tools and scale up your solutions\u003c\/li\u003e\n\u003cli\u003eGet to grips with Apache Spark, Kubernetes, and Ray\u003c\/li\u003e\n\u003cli\u003eBuild and run ML pipelines with Apache Airflow, ZenML, and Kubeflow\u003c\/li\u003e\n\u003cli\u003eDetect drift and build retraining mechanisms into your solutions\u003c\/li\u003e\n\u003cli\u003eImprove error handling with control flows and vulnerability scanning\u003c\/li\u003e\n\u003cli\u003eHost and build ML microservices and batch processes running on AWS\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\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\u003eTable of Contents\u003col\u003e\n\u003cli\u003eIntroduction to ML Engineering\u003c\/li\u003e\n\u003cli\u003eThe Machine Learning Development Process\u003c\/li\u003e\n\u003cli\u003eFrom Model to Model Factory \u003c\/li\u003e\n\u003cli\u003ePackaging Up\u003c\/li\u003e\n\u003cli\u003eDeployment Patterns and Tools\u003c\/li\u003e\n\u003cli\u003eScaling Up\u003c\/li\u003e\n\u003cli\u003eDeep Learning, Generative AI, and LLMOps \u003c\/li\u003e\n\u003cli\u003eBuilding an Example ML Microservice\u003c\/li\u003e\n\u003cli\u003eBuilding an Extract, Transform, Machine Learning Use Case\u003c\/li\u003e\n\u003c\/ol\u003e\u003cbr\u003e","brand":"Packt Publishing","offers":[{"title":"Default Title","offer_id":50526268850450,"sku":"9781837631964","price":45.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_c37b5296-c18a-4b9f-a110-971886ee8032.jpg?v=1731267006","url":"https:\/\/surprise-castle.myshopify.com\/products\/machine-learning-engineering-with-python-second-edition-manage-the-lifecycle-of-machine-learning-models-using-mlops-with-practical-examples-9781837631964","provider":"Surprise Castle","version":"1.0","type":"link"}