{"product_id":"154627","title":"MLOps Practical Guide ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPLQhVGYVtDGme5yTtfyPf8UXNj.png?v=1765081051\" 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 MLOps Practical Guide \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\/119859717\/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\u003e* MLOps engineering know-how for stable operation of machine learning models and successful CI\/CD\u003cbr\u003e * Includes tips for writing an MLOps portfolio and interviews with MLOps practitioners.\u003cbr\u003e * Added translator's notes based on the latest content and reflected source code refactoring\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e This book provides a comprehensive understanding of MLOps and DevOps concepts and includes various practical exercises to facilitate in-depth learning. \u003cbr\u003eWe cover deployment methods for stable operation of machine learning models, as well as key technology areas of MLOps, including AutoML, containers, edge computing, and model portability.\u003cbr\u003e In addition, we provide hands-on training to help you gain MLOps experience on various cloud platforms, including AWS, Azure, and GCP.\u003cbr\u003e It also introduces MLOps cases based on the author's real-life experience and interviews with MLOps practitioners.\u003cbr\u003e The appendix provides considerations for implementing MLOps, interview questions to prepare for an MLOps career, and tips for writing a technical portfolio to help you easily apply the knowledge in practice.\u003cbr\u003e\n\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\u003eCHAPTER 1: Invitation to the World of MLOps\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _1.1 The Rise of Machine Learning Engineers and MLOps\u003cbr\u003e _1.2 What is MLOps?\u003cbr\u003e _1.3 DevOps and MLOps\u003cbr\u003e _1.4 MLOps Hierarchy of Needs Theory\u003cbr\u003e __1.4.1 DevOps Implementation \u003cbr\u003e__1.4.2 Configuring continuous integration using GitHub Actions\u003cbr\u003e __1.4.3 DataOps and Data Engineering\u003cbr\u003e __1.4.4 Platform Automation\u003cbr\u003e __1.4.5 MLOps\u003cbr\u003e _1.5 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2 Basic Concepts for Getting Started with MLOps\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _2.1 Bash and the Linux Command Line\u003cbr\u003e _2.2 Cloud Shell Development Environment\u003cbr\u003e _2.3 Bash shell and commands\u003cbr\u003e __2.3.1 File List\u003cbr\u003e __2.3.2 Execution Command\u003cbr\u003e __2.3.3 File Navigation\u003cbr\u003e __2.3.4 Shell Input\/Output\u003cbr\u003e __2.3.5 Shell Settings\u003cbr\u003e __2.3.6 Writing a shell script\u003cbr\u003e _2.4 Cloud Computing Foundation and Components\u003cbr\u003e _2.5 Getting Started with Cloud Computing\u003cbr\u003e _2.6 Python Crash Course\u003cbr\u003e _2.7 Python Tutorial for Minimalists\u003cbr\u003e _2.8 Math Crash Course for Programmers\u003cbr\u003e __2.8.1 Descriptive Statistics and the Normal Distribution\u003cbr\u003e __2.8.2 Optimization\u003cbr\u003e __[Translator's Note]\u003cbr\u003e _2.9 Core Concepts of Machine Learning\u003cbr\u003e _2.10 Trying Data Science\u003cbr\u003e _2.11 Building a Simple Pipeline from Scratch\u003cbr\u003e _2.12 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e \u003cbr\u003e\u003cb\u003eCHAPTER 3 MLOps for Containers and Edge Devices\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _3.1 Container\u003cbr\u003e __3.1.1 Container Runtime\u003cbr\u003e __3.1.2 Creating a container\u003cbr\u003e __3.1.3 Running a container\u003cbr\u003e __3.1.4 Container Best Practices\u003cbr\u003e __3.1.5 Serving Models via HTTP\u003cbr\u003e _3.2 Edge Devices\u003cbr\u003e __3.2.1 Google Coral\u003cbr\u003e __3.2.2 Azure Percept\u003cbr\u003e __3.2.3 TensorFlow Hub\u003cbr\u003e __3.2.4 Google Coral Edge TPU Compiler\u003cbr\u003e _3.3 Containers for Fully Managed Machine Learning Systems\u003cbr\u003e __3.3.1 Trading MLOps Containers\u003cbr\u003e __3.3.2 Containers in various uses\u003cbr\u003e _3.4 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 4: Implementing Continuous Delivery in Machine Learning Applications\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _4.1 Packaging Machine Learning Models\u003cbr\u003e _4.2 Code-as-infrastructure for continuous deployment of machine learning models\u003cbr\u003e _4.3 Using Cloud Pipelines\u003cbr\u003e __4.3.1 Controlling Model Distribution\u003cbr\u003e __4.3.2 Testing Strategy for Model Deployment\u003cbr\u003e _4.4 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5 AutoML and KaizenML\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _5.1 AutoML\u003cbr\u003e __5.1.1 MLOps Industrial Revolution \u003cbr\u003e__5.1.2 AutoML vs. KaizenML\u003cbr\u003e __5.1.3 Feature Store\u003cbr\u003e _5.2 Apple Ecosystem\u003cbr\u003e __5.2.1 Apple's AutoML: Create ML\u003cbr\u003e __5.2.2 Apple's Core ML\u003cbr\u003e _5.3 Google's AutoML and Edge Computer Vision\u003cbr\u003e _5.4 AutoML on Azure\u003cbr\u003e _5.5 AWS AutoML\u003cbr\u003e _5.6 Open Source AutoML\u003cbr\u003e __5.6.1 Ludwig\u003cbr\u003e __5.6.2 FLAML\u003cbr\u003e _5.7 Model Explanation Power\u003cbr\u003e _5.8 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6 MONITORING AND LOGGING\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _6.1 Cloud MLOps and Observability\u003cbr\u003e __[Translator's Note]\u003cbr\u003e _6.2 Logging Basics\u003cbr\u003e _6.3 Practicing Logging in Python\u003cbr\u003e __[Translator's Note]\u003cbr\u003e __6.3.1 Setting the log level\u003cbr\u003e __6.3.2 Logging Multiple Applications Simultaneously\u003cbr\u003e _6.4 Monitoring and Observability\u003cbr\u003e __6.4.1 Basics of Model Monitoring\u003cbr\u003e __6.4.2 Monitoring Drift in AWS SageMaker\u003cbr\u003e _6.5 Monitoring Drift in Azure Machine Learning\u003cbr\u003e _6.6 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7 MLOps with AWS\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _7.1 Getting Started with AWS\u003cbr\u003e __7.1.1 Trying AWS Products\u003cbr\u003e __7.1.2 AWS and MLOps\u003cbr\u003e _7.2 MLOps Recipe Using AWS \u003cbr\u003e__7.2.1 Command Line Interface Tools\u003cbr\u003e __7.2.2 Flask Microservices\u003cbr\u003e _7.3 AWS Lambda Recipe\u003cbr\u003e __7.3.1 AWS Lambda-SAM: Using it in a Local Environment\u003cbr\u003e __7.3.2 AWS Lambda-SAM: Containerizing and Deploying\u003cbr\u003e _7.4 AWS Machine Learning Products and Advice for Solving Real-World Problems\u003cbr\u003e __[Interview] Case Study of a Sports SNS Service\u003cbr\u003e __[Interview] Career Advice from AWS Machine Learning Evangelist Julien\u003cbr\u003e _7.5 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 Azure Environment and MLOps\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _8.1 Azure CLI and Python SDK\u003cbr\u003e _8.2 Certification\u003cbr\u003e __8.2.1 Service Subject\u003cbr\u003e __8.2.2 API Service Authentication\u003cbr\u003e _8.3 Compute Instance\u003cbr\u003e _8.4 distribution\u003cbr\u003e __8.4.1 Model Registration\u003cbr\u003e __8.4.2 Dataset Version Management\u003cbr\u003e __[Translator's Note]\u003cbr\u003e _8.5 Deploying Models to a Compute Cluster\u003cbr\u003e __8.5.1 Configuring a Cluster\u003cbr\u003e __8.5.2 Deploying the Model\u003cbr\u003e _8.6 Troubleshooting Deployment Issues\u003cbr\u003e __8.6.1 Searching logs\u003cbr\u003e __8.6.2 Application Insights\u003cbr\u003e __8.6.3 Debugging in a local environment\u003cbr\u003e _8.7 Azure Machine Learning Pipeline \u003cbr\u003e__8.7.1 Publishing Pipeline\u003cbr\u003e __8.7.2 Azure Machine Learning Designer\u003cbr\u003e _8.8 Machine Learning Life Cycle\u003cbr\u003e _8.9 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 9 Google Cloud Platform and Kubernetes\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _9.1 Google Cloud Platform Overview\u003cbr\u003e __9.1.1 Continuous Integration and Continuous Deployment\u003cbr\u003e __9.1.2 hello world kubernetes\u003cbr\u003e __9.1.3 Cloud-Native Database Selection and Design\u003cbr\u003e _9.2 DataOps on Google Cloud Platform\u003cbr\u003e _9.3 Machine Learning Model Operation\u003cbr\u003e _9.4 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 10 Machine Learning Interoperability\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _10.1 Why Interoperability Matters\u003cbr\u003e _10.2 ONNX: Open Neural Network Exchange\u003cbr\u003e __10.2.1 ONNX Model Zoo\u003cbr\u003e __10.2.2 Converting PyTorch to ONNX\u003cbr\u003e __10.2.3 Converting TensorFlow to ONNX\u003cbr\u003e __10.2.4 Deploying ONNX Models on Azure\u003cbr\u003e _10.3 Apple's Core ML and ONNX\u003cbr\u003e _10.4 Edge Integration\u003cbr\u003e _10.5 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 11 MLOps Command-Line Tools and Microservices Building\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _11.1 Python Packaging \u003cbr\u003e_11.2 Requirements file\u003cbr\u003e _11.3 Command Line Tools\u003cbr\u003e __11.3.1 Creating a dataset linter\u003cbr\u003e __11.3.2 Command-line tool modularization\u003cbr\u003e _11.4 Microservices\u003cbr\u003e __11.4.1 Creating a Serverless Function\u003cbr\u003e __11.4.2 Cloud Function Authentication\u003cbr\u003e __11.4.3 Building a Cloud-Based Command-Line Interface\u003cbr\u003e _11.5 Machine Learning Command Line Interface Workflow\u003cbr\u003e _11.6 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 12 MLOps Case Studies\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _12.1 The Unexpected Benefits of Ignorance in Machine Learning\u003cbr\u003e _12.2 MLOps Project for Sports Social Networks\u003cbr\u003e __12.2.1 Mechanical Repetitive Task: Data Labeling\u003cbr\u003e __12.2.2 Influencer Level\u003cbr\u003e __12.2.3 Artificial Intelligence Products\u003cbr\u003e _12.3 Reality vs. Perfect Technology\u003cbr\u003e _12.4 Important Challenges of MLOps\u003cbr\u003e __12.4.1 Ethical Issues and Unintended Consequences\u003cbr\u003e __12.4.2 Lack of operational capabilities\u003cbr\u003e __12.4.3 Should we focus on technology or business?\u003cbr\u003e __[Interview] Piero Molino, MLOps Practitioner\u003cbr\u003e __[Interview] MLOps Practitioner Francesca Lazzeri  \u003cbr\u003e_12.5 Final Recommendations for Implementing MLOps\u003cbr\u003e __12.5.1 Data Governance and Cybersecurity\u003cbr\u003e __12.5.2 Frequently mentioned concepts and tools when implementing MLOps\u003cbr\u003e _12.6 In conclusion\u003cbr\u003e Try it out\u003cbr\u003e Think about it\u003cbr\u003e\u003cbr\u003e Appendix A.\u003cbr\u003e technical qualifications\u003cbr\u003e Appendix B. Tips for Creating a Technology Portfolio for MLOps\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\/TopCate4228\/MidCate009\/422789422.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 MLOps is known as DevOps evolved for the AI ​​era.\u003cbr\u003e Simply put, MLOps is the process of automating machine learning using DevOps methodologies.\u003cbr\u003e MLOps follows in the footsteps of DevOps in emphasizing the importance of automation, one of the core philosophies of DevOps.\u003cbr\u003e\u003cbr\u003e \u003cbr\u003e\"MLOps Practical Guide\" comprehensively covers not only the theory of MLOps and DevOps, but also how to deploy and manage machine learning models in real-world operating environments, continuous integration (CI) and continuous delivery (CD), which are essential elements of automation, and even the concept of Kaizen, which means continuous improvement.\u003cbr\u003e It also includes practical examples of MLOps implementation on platforms such as AWS, Azure, and Google Cloud Platform, as well as practical examples of MLOps from the translator's own experience.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e This book aims to provide insightful explanations of the concepts of MLOps.\u003cbr\u003e Rather than using ChatGPT for translation, our translators directly translate the original text and refactor the source code to provide hands-on practice code for each case.\u003cbr\u003e Additionally, the 'Translator's Note' in the text is an explanation added directly by the translator by referring to the latest information to help readers understand better. \u003cbr\u003eAt the end of each chapter, you can review what you've learned, think critically, and gain various insights into MLOps through 'Practice' and 'Think' exercises.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e - Software developers who need to write documentation along with MLOps code\u003cbr\u003e - Developers who want to experience MLOps on various platforms\u003cbr\u003e - Developers who are curious about engineering for actual service deployment along with machine learning development. \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 July 7, 2023\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 496 pages | 880g | 183*235*20mm\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 9791169211215\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 1169211216 \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":43893438971946,"sku":"154627","price":49.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/6f2054d35ab25e25e07dc075408d5227.jpg?v=1765402144","url":"https:\/\/librairie.coreenne.fr\/en\/products\/154627","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}