{"product_id":"140193","title":"Machine Learning Engineering with Python ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYGWlW4TztUUTX3gvyiXZWQevGssS.png?v=1765078653\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Machine Learning Engineering with Python \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/145402000\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eManaging the production lifecycle of machine learning models with practical examples using ML Ops!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Machine Learning Engineering with Python is a practical guide that helps ML Ops (MLOps) engineers and machine learning (ML) engineers build solutions to real-world problems.\u003cbr\u003e This book provides the skills you need to stay ahead in this rapidly evolving field. \u003cbr\u003eThis book uses an example-based approach to help you develop your skills, covering essential technical concepts, implementation patterns, and development methodologies. You'll explore the key stages of the ML development lifecycle and learn how to create a standardized \"model factory\" for model training and retraining. You'll also learn how to leverage CI\/CD concepts and detect various types of drift.\u003cbr\u003e\u003cbr\u003e This book also teaches you how to practice modern deployment architectures and scale your solutions.\u003cbr\u003e We delve into all aspects of ML engineering and MLops, focusing on the latest open source and cloud-based technologies.\u003cbr\u003e It includes a completely new approach to advanced pipeline and orchestration techniques.\u003cbr\u003e In chapters covering deep learning, generative AI, and LLMops, you'll learn analytical methods that leverage the powerful capabilities of LLM using tools like LangChain, PyTorch, and Hugging Face. \u003cbr\u003eLearn how to boost your productivity with AI assistants like GitHub Copilot, and delve deeper into engineering considerations for deep learning work.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e▣ Chapter 1: Introduction to Machine Learning Engineering\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 1.1 Technical Requirements\u003cbr\u003e 1.2 Data-related job classification\u003cbr\u003e __1.2.1 Data Scientist\u003cbr\u003e __1.2.2 ML Engineer\u003cbr\u003e __1.2.3 ML Ops Engineer\u003cbr\u003e __1.2.4 Data Engineer\u003cbr\u003e 1.3 Working as an effective team\u003cbr\u003e 1.4 Machine Learning Engineering in Real-World Environments\u003cbr\u003e 1.5 What does a machine learning solution look like?\u003cbr\u003e __1.5.1 Why Python?\u003cbr\u003e 1.6 High-Level Machine Learning System Design\u003cbr\u003e __1.6.1 Example 1: Batch Anomaly Detection Service\u003cbr\u003e __1.6.2 Example 2: Prediction API\u003cbr\u003e __1.6.3 Example 3: Classification Pipeline\u003cbr\u003e 1.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 2: Machine Learning Development Process\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 2.1 Technical Requirements\u003cbr\u003e 2.2 Tool Settings\u003cbr\u003e __2.2.1 AWS Account Settings  \u003cbr\u003e2.3 Four Steps from Concept to Solution\u003cbr\u003e __2.3.1 Comparison with CRISP-DM\u003cbr\u003e __2.3.2 discovered\u003cbr\u003e __2.3.3 Play\u003cbr\u003e __2.3.4 Development\u003cbr\u003e __2.3.5 distribution\u003cbr\u003e 2.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 3: From Model to Model Factory\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 3.1 Technical Requirements\u003cbr\u003e 3.2 Defining a Model Factory\u003cbr\u003e 3.3 What is learning?\u003cbr\u003e __3.3.1 Defining Goals\u003cbr\u003e __3.3.2 Minimizing Losses\u003cbr\u003e __3.3.3 Preparing Data\u003cbr\u003e 3.4 Feature Engineering for Machine Learning\u003cbr\u003e __3.4.1 Handling Categorical Features\u003cbr\u003e __3.4.2 Handling numeric features\u003cbr\u003e 3.5 Designing a Training System\u003cbr\u003e __3.5.1 Training System Design Options\u003cbr\u003e __3.5.2 Training-Run\u003cbr\u003e __3.5.3 Training-Storage\u003cbr\u003e 3.6 Drift and Retraining\u003cbr\u003e __3.6.1 Data Drift Detection\u003cbr\u003e __3.6.2 Detecting Concept Drift\u003cbr\u003e __3.6.3 Setting limits\u003cbr\u003e __3.6.4 Diagnosing Drift\u003cbr\u003e __3.6.5 Drift Countermeasures\u003cbr\u003e __3.6.6 Other tools for monitoring\u003cbr\u003e __3.6.7 Automating Training\u003cbr\u003e __3.6.8 Hierarchy of Automation\u003cbr\u003e __3.6.9 Hyperparameter Optimization\u003cbr\u003e 3.6.10 AutoML\u003cbr\u003e 3.7 Persisting the Model  \u003cbr\u003e3.8 Building a Model Factory with Pipelines\u003cbr\u003e __3.8.1 Scikit-learn Pipeline\u003cbr\u003e __3.8.2 Spark ML Pipeline\u003cbr\u003e 3.9 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 4: Packaging\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 4.1 Technical Requirements\u003cbr\u003e 4.2 Writing Good Python Code\u003cbr\u003e __4.2.1 Building the Basics of Python\u003cbr\u003e __4.2.2 Useful Techniques\u003cbr\u003e __4.2.3 Python Coding Conventions\u003cbr\u003e __4.2.4 PySpark Coding Style\u003cbr\u003e 4.3 Choosing a Coding Style\u003cbr\u003e __4.3.1 Object-Oriented Programming\u003cbr\u003e __4.3.2 Functional Programming\u003cbr\u003e 4.4 Packaging the Code\u003cbr\u003e __4.4.1 Why create a package?\u003cbr\u003e __4.4.2 What code should I package?\u003cbr\u003e __4.4.3 Designing a Package\u003cbr\u003e 4.5 Building the Package\u003cbr\u003e __4.5.1 Managing the environment with Makefile\u003cbr\u003e __4.5.2 Getting Started with Poetry\u003cbr\u003e 4.6 Testing, Logging, Security, and Error Handling\u003cbr\u003e __4.6.1 Testing\u003cbr\u003e __4.6.2 Solution Security\u003cbr\u003e __4.6.3 Analyzing security issues in code\u003cbr\u003e __4.6.4 Security check of dependent packages\u003cbr\u003e __4.6.5 Logging\u003cbr\u003e __4.6.6 Error Handling\u003cbr\u003e 4.7 Don't reinvent the wheel\u003cbr\u003e 4.8 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 5: Deployment Patterns and Tools\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e5.1 Technical Requirements\u003cbr\u003e 5.2 System Design\u003cbr\u003e __5.2.1 System Design Principles\u003cbr\u003e 5.3 Representative machine learning patterns\u003cbr\u003e __5.3.1 Data Lake\u003cbr\u003e __5.3.2 Microservices\u003cbr\u003e __5.3.3 Event-driven design\u003cbr\u003e __5.3.4 Batch Processing\u003cbr\u003e 5.4 Containerization\u003cbr\u003e 5.5 Hosting Your Own Microservices on AWS\u003cbr\u003e __5.5.1 Pushing to ECR\u003cbr\u003e __5.5.2 Deploying to ECS\u003cbr\u003e 5.6 Building a General Pipeline Using Airflow\u003cbr\u003e __5.6.1 Airflow\u003cbr\u003e __5.6.2 MWAA\u003cbr\u003e __5.6.3 Building a CI\/CD Pipeline for Airflow\u003cbr\u003e 5.7 Building Advanced ML Pipelines\u003cbr\u003e __5.7.1 ZenML\u003cbr\u003e __5.7.2 Kubeflow\u003cbr\u003e 5.8 Choosing a Distribution Strategy\u003cbr\u003e 5.9 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 6: Scaling\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 6.1 Technical Requirements\u003cbr\u003e 6.2 Extending with Spark\u003cbr\u003e __6.2.1 Spark Tips and Tricks\u003cbr\u003e __6.2.2 Spark on the Cloud, AWS EMR\u003cbr\u003e 6.3 Building a Serverless Infrastructure\u003cbr\u003e 6.4 Large-Scale Containerization with Kubernetes\u003cbr\u003e 6.5 Extending to Ray\u003cbr\u003e __6.5.1 Getting Started with Ray for ML\u003cbr\u003e __6.5.2 Ray's Computational Extensions\u003cbr\u003e __6.5.3 Extending the Serving Layer Using Ray  \u003cbr\u003e6.6 Large-scale system design\u003cbr\u003e 6.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 7: Deep Learning, Generative AI, and LLMops\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 7.1 Deep Learning\u003cbr\u003e __7.1.1 PyTorch Basics\u003cbr\u003e __7.1.2 Deep Learning Extension and Production Applications\u003cbr\u003e __7.1.3 Fine-tuning and Transfer Learning\u003cbr\u003e 7.2 Large-scale language models\u003cbr\u003e __7.2.1 LLM Basic Concepts and Structure\u003cbr\u003e __7.2.2 LLM Utilization via API\u003cbr\u003e __7.2.3 Coding with LLM\u003cbr\u003e 7.3 LLM Verification and Prompt Management\/Operation\u003cbr\u003e __7.3.1 Verifying LLM\u003cbr\u003e __7.3.2 PromptOps\u003cbr\u003e 7.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003e▣ Chapter 8: Building Example ML Microservices\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 8.1 Technical Requirements\u003cbr\u003e 8.2 Understanding the Prediction Problem\u003cbr\u003e 8.3 Predictive Service Design\u003cbr\u003e 8.4 Tool Selection\u003cbr\u003e 8.5 Training Scaling\u003cbr\u003e 8.6 Serving Models with FastAPI\u003cbr\u003e __8.6.1 Response and Request Schema\u003cbr\u003e __8.6.2 Managing Models in Microservices\u003cbr\u003e __8.6.3 Integrating All Components\u003cbr\u003e 8.7 Containerization and Deployment with Kubernetes\u003cbr\u003e __8.7.1 Application Containerization\u003cbr\u003e __8.7.2 Scaling with Kubernetes\u003cbr\u003e __8.7.3 Deployment Strategy\u003cbr\u003e 8.8 Summary\u003cbr\u003e \u003cbr\u003e\u003cb\u003e▣ Chapter 9: ETML (Extraction, Transformation, Machine Learning) Case Study\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e 9.1 Technical Requirements\u003cbr\u003e 9.2 Understanding Batch Processing Issues\u003cbr\u003e 9.3 ETML Solution Design\u003cbr\u003e 9.4 Selecting a Tool\u003cbr\u003e __9.4.1 Interfaces and Repositories\u003cbr\u003e __9.4.2 Model Extension\u003cbr\u003e __9.4.3 ETML Pipeline Scheduling\u003cbr\u003e Running the 9.5 build\u003cbr\u003e __9.5.1 Building an ETML Pipeline Using Advanced Airflow Features\u003cbr\u003e 9.6 Summary\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5268\/MidCate008\/526778843.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003e★ What this book covers ★\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ◎ Planning and managing end-to-end ML development projects\u003cbr\u003e ◎ Deep learning, LLM, and LLMops for utilizing generative AI\u003cbr\u003e ◎ Packaging ML tools and expanding solutions using Python\u003cbr\u003e ◎ Utilizing Apache Spark, Kubernetes, and Ray\u003cbr\u003e ◎ Building and executing ML pipelines using Apache Airflow, ZenML, and Kubeflow\u003cbr\u003e ◎ Integrating drift detection and retraining mechanisms into the solution\u003cbr\u003e ◎ Improved error handling through control flow and vulnerability scanning \u003cbr\u003e◎ Building and operating ML microservices and batch processes using AWS \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e April 24, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 392 pages | 188*240*16mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791158396046\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 115839604X \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893404827690,"sku":"140193","price":41.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/3b79d1bd0b608b21ab29c85f152a5a60.jpg?v=1765400561","url":"https:\/\/librairie.coreenne.fr\/en\/products\/140193","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}