Practical MLOps: operationalizing machine learning models
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Sebastopol, CA
O'Reilly Media
[2021]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 439 Seiten Illustrationen, Diagramme |
| ISBN: | 9781098103019 |
Internformat
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Datensatz im Suchindex
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| adam_text | Table of Contents Preface....................................................................................................... ix 1. Introduction to MLOps................................................................................ 1 Rise of the Machine Learning Engineer and MLOps 2 What Is MLOps? 4 DevOps and MLOps 5 An MLOps Hierarchy of Needs 7 Implementing DevOps 8 Configuring Continuous Integration with GitHub Actions 13 DataOps and Data Engineering 15 Platform Automation 16 MLOps 17 Conclusion 20 Exercises 21 Critical Thinking Discussion Questions 22 2. MLOps Foundations.................................................................................... 23 Bash and the Linux Command Line 23 Cloud Shell Development Environments 24 Bash Shell and Commands 26 List Files 26 Run Commands 26 Files and Navigation 27 Input/Output 27 Configuration 28 Writing a Script 28 Cloud Computing Foundations and Building Blocks 29 Getting Started with Cloud Computing 31 iii
Python Crash Course Minimalistic Python Tutorial Math for Programmers Crash Course Descriptive Statistics and Normal Distributions Optimization Machine Learning Key Concepts Doing Data Science Build an MLOps Pipeline from Zero Conclusion Exercises Critical Thinking Discussion Questions 33 36 37 37 41 50 54 56 63 64 64 3. MLOps for Containers and Edge Devices........................................................... 67 Containers Container Runtime Creating a Container Running a Container Best Practices Serving a Trained Model Over HTTP Edge Devices Coral Azure Percept TFHub Porting Over Non-TPU Models Containers for Managed ML Systems Containers in Monetizing MLOps Build Once, Run Many MLOps Workflow Conclusion Exercises Critical Thinking Discussion Questions 68 69 69 72 74 76 80 81 84 85 86 89 90 91 91 92 92 4. Continuous Delivery for Machine Learning Models........................................... 93 Packaging for ML Models Infrastructure as Code for Continuous Delivery of ML Models Using Cloud Pipelines Controlled Rollout of Models Testing Techniques for Model Deployment Conclusion Exercises Critical Thinking Discussion Questions iv I Table of Contents 95 99 107 110 112 115 116 116
5. AutoML and KaizenML....................................................... AutoML MLOps Industrial Revolution Kaizen Versus KaizenML Feature Stores Apple’s Ecosystem Apple’s AutoML: Create ML Apple’s Core ML Tools Google’s AutoML and Edge Computer Vision Azure’s AutoML AWS AutoML Open Source AutoML Solutions Ludwig FLAML Model Explainability Conclusion Exercises Critical Thinking Discussion Questions 117 118 123 125 127 131 132 136 139 144 146 151 151 153 154 158 159 159 6. Monitoring and Logging.............................................................................161 Observability for Cloud MLOps Introduction to Logging Logging in Python Modifying Log Levels Logging Different Applications Monitoring and Observability Basics of Model Monitoring Monitoring Drift with AWS SageMaker Monitoring Drift with Azure ML Conclusion Exercises Critical Thinking Discussion Questions 163 164 165 169 170 172 174 175 182 184 185 185 7. MLOps for AWS........................................................................................ 187 Introduction to AWS Getting Started with AWS Services MLOps on AWS MLOps Cookbook on AWS CLI Tools Flask Microservice AWS Lambda Recipes AWS Lambda-SAM Local 188 189 206 209 211 218 223 223 Table of Contents | v
AWS Lambda-SAM Containerized Deploy Applying AWS Machine Learning to the Real World Conclusion Exercises Critical Thinking Discussion Questions 224 229 233 234 234 MLOps for Azure.................................................................... ................ 235 Azure CLI and Python SDK Authentication Service Principal Authenticating API Services Compute Instances Deploying Registering Models Versioning Datasets Deploying Models to a Compute Cluster Configuring a Cluster Deploying a Model Troubleshooting Deployment Issues Retrieving Logs Application Insights Debugging Locally Azure ML Pipelines Publishing Pipelines Azure Machine Learning Designer ML Lifecycle Conclusion Exercises Critical Thinking Discussion Questions 236 238 238 240 240 242 243 245 246 246 248 251 252 253 254 257 259 260 262 263 263 264 MLOps for GCP....................................................................... ............... 265 Google Cloud Platform Overview Continuous Integration and Continuous Delivery Kübemetes Hello World Cloud Native Database Choice and Design DataOps on GCP: Applied Data Engineering Operationalizing ML Models Conclusion Exercises Critical Thinking Discussion Questions vi ļ Table of Contents 265 270 272 280 282 287 289 291 291
10. Machine Learning Interoperability................................................................ 293 Why Interoperability Is Critical ONNX: Open Neural Network Exchange ONNX Model Zoo Convert PyTorch into ONNX Create a Generic ONNX Checker Convert TensorFlow into ONNX Deploy ONNX to Azure Apple Core ML Edge Integration Conclusion Exercises Critical Thinking Discussion Questions 294 296 297 299 301 303 307 310 314 315 316 316 11. Building MLOps Command Line Tools and Microservices..................................... 317 Python Packaging The Requirements File Command Line Tools Creating a Dataset Linter Modularizing a Command Line Tool Microservices Creating a Serverless Function Authenticating to Cloud Functions Building a Cloud-Based CLI Machine Learning CLI Workflows Conclusion Exercises Critical Thinking Discussion Questions 319 320 321 321 328 331 333 338 341 342 344 345 345 12. Machine Learning Engineering and MLOps Case Studies..................................... 347 Unlikely Benefits of Ignorance in Building Machine Learning Models MLOps Projects at Sqor Sports Social Network Mechanical Turk Data Labeling Influencer Rank Athlete Intelligence (AI Product) The Perfect Technique Versus the Real World Critical Challenges in MLOps Ethical and Unintended Consequences Lack of Operational Excellence Focus on Prediction Accuracy Versus the Big Picture Final Recommendations to Implement MLOps Data Governance and Cybersecurity Table of Contents 348 350 351 352 353 355 357 358 358 359 364 365 | vii
MLOps Design Patterns Conclusion Exercises Critical Thinking Discussion Questions 366 367 367 368 A. Key Terms.............................................................................................. 369 B. Technology Certifications........................................................................... 375 C Remote Work.................................................................................... 393 D. Think Like a VC for Your Career...................................................................... 399 E. Building a Technical Portfolio for MLOps....................................................... 403 F. Data Science Case Study: Intermittent Fasting................................................ 409 G. Additional Educational Resources................................................................ 415 H. Technical Project Management................................................................... 427 Index......................................................................................................... 431
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| adam_txt |
Table of Contents Preface. ix 1. Introduction to MLOps. 1 Rise of the Machine Learning Engineer and MLOps 2 What Is MLOps? 4 DevOps and MLOps 5 An MLOps Hierarchy of Needs 7 Implementing DevOps 8 Configuring Continuous Integration with GitHub Actions 13 DataOps and Data Engineering 15 Platform Automation 16 MLOps 17 Conclusion 20 Exercises 21 Critical Thinking Discussion Questions 22 2. MLOps Foundations. 23 Bash and the Linux Command Line 23 Cloud Shell Development Environments 24 Bash Shell and Commands 26 List Files 26 Run Commands 26 Files and Navigation 27 Input/Output 27 Configuration 28 Writing a Script 28 Cloud Computing Foundations and Building Blocks 29 Getting Started with Cloud Computing 31 iii
Python Crash Course Minimalistic Python Tutorial Math for Programmers Crash Course Descriptive Statistics and Normal Distributions Optimization Machine Learning Key Concepts Doing Data Science Build an MLOps Pipeline from Zero Conclusion Exercises Critical Thinking Discussion Questions 33 36 37 37 41 50 54 56 63 64 64 3. MLOps for Containers and Edge Devices. 67 Containers Container Runtime Creating a Container Running a Container Best Practices Serving a Trained Model Over HTTP Edge Devices Coral Azure Percept TFHub Porting Over Non-TPU Models Containers for Managed ML Systems Containers in Monetizing MLOps Build Once, Run Many MLOps Workflow Conclusion Exercises Critical Thinking Discussion Questions 68 69 69 72 74 76 80 81 84 85 86 89 90 91 91 92 92 4. Continuous Delivery for Machine Learning Models. 93 Packaging for ML Models Infrastructure as Code for Continuous Delivery of ML Models Using Cloud Pipelines Controlled Rollout of Models Testing Techniques for Model Deployment Conclusion Exercises Critical Thinking Discussion Questions iv I Table of Contents 95 99 107 110 112 115 116 116
5. AutoML and KaizenML. AutoML MLOps Industrial Revolution Kaizen Versus KaizenML Feature Stores Apple’s Ecosystem Apple’s AutoML: Create ML Apple’s Core ML Tools Google’s AutoML and Edge Computer Vision Azure’s AutoML AWS AutoML Open Source AutoML Solutions Ludwig FLAML Model Explainability Conclusion Exercises Critical Thinking Discussion Questions 117 118 123 125 127 131 132 136 139 144 146 151 151 153 154 158 159 159 6. Monitoring and Logging.161 Observability for Cloud MLOps Introduction to Logging Logging in Python Modifying Log Levels Logging Different Applications Monitoring and Observability Basics of Model Monitoring Monitoring Drift with AWS SageMaker Monitoring Drift with Azure ML Conclusion Exercises Critical Thinking Discussion Questions 163 164 165 169 170 172 174 175 182 184 185 185 7. MLOps for AWS. 187 Introduction to AWS Getting Started with AWS Services MLOps on AWS MLOps Cookbook on AWS CLI Tools Flask Microservice AWS Lambda Recipes AWS Lambda-SAM Local 188 189 206 209 211 218 223 223 Table of Contents | v
AWS Lambda-SAM Containerized Deploy Applying AWS Machine Learning to the Real World Conclusion Exercises Critical Thinking Discussion Questions 224 229 233 234 234 MLOps for Azure. . 235 Azure CLI and Python SDK Authentication Service Principal Authenticating API Services Compute Instances Deploying Registering Models Versioning Datasets Deploying Models to a Compute Cluster Configuring a Cluster Deploying a Model Troubleshooting Deployment Issues Retrieving Logs Application Insights Debugging Locally Azure ML Pipelines Publishing Pipelines Azure Machine Learning Designer ML Lifecycle Conclusion Exercises Critical Thinking Discussion Questions 236 238 238 240 240 242 243 245 246 246 248 251 252 253 254 257 259 260 262 263 263 264 MLOps for GCP. . 265 Google Cloud Platform Overview Continuous Integration and Continuous Delivery Kübemetes Hello World Cloud Native Database Choice and Design DataOps on GCP: Applied Data Engineering Operationalizing ML Models Conclusion Exercises Critical Thinking Discussion Questions vi ļ Table of Contents 265 270 272 280 282 287 289 291 291
10. Machine Learning Interoperability. 293 Why Interoperability Is Critical ONNX: Open Neural Network Exchange ONNX Model Zoo Convert PyTorch into ONNX Create a Generic ONNX Checker Convert TensorFlow into ONNX Deploy ONNX to Azure Apple Core ML Edge Integration Conclusion Exercises Critical Thinking Discussion Questions 294 296 297 299 301 303 307 310 314 315 316 316 11. Building MLOps Command Line Tools and Microservices. 317 Python Packaging The Requirements File Command Line Tools Creating a Dataset Linter Modularizing a Command Line Tool Microservices Creating a Serverless Function Authenticating to Cloud Functions Building a Cloud-Based CLI Machine Learning CLI Workflows Conclusion Exercises Critical Thinking Discussion Questions 319 320 321 321 328 331 333 338 341 342 344 345 345 12. Machine Learning Engineering and MLOps Case Studies. 347 Unlikely Benefits of Ignorance in Building Machine Learning Models MLOps Projects at Sqor Sports Social Network Mechanical Turk Data Labeling Influencer Rank Athlete Intelligence (AI Product) The Perfect Technique Versus the Real World Critical Challenges in MLOps Ethical and Unintended Consequences Lack of Operational Excellence Focus on Prediction Accuracy Versus the Big Picture Final Recommendations to Implement MLOps Data Governance and Cybersecurity Table of Contents 348 350 351 352 353 355 357 358 358 359 364 365 | vii
MLOps Design Patterns Conclusion Exercises Critical Thinking Discussion Questions 366 367 367 368 A. Key Terms. 369 B. Technology Certifications. 375 C Remote Work. 393 D. Think Like a VC for Your Career. 399 E. Building a Technical Portfolio for MLOps. 403 F. Data Science Case Study: Intermittent Fasting. 409 G. Additional Educational Resources. 415 H. Technical Project Management. 427 Index. 431 |
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| title | Practical MLOps operationalizing machine learning models |
| title_auth | Practical MLOps operationalizing machine learning models |
| title_exact_search | Practical MLOps operationalizing machine learning models |
| title_exact_search_txtP | Practical MLOps operationalizing machine learning models |
| title_full | Practical MLOps operationalizing machine learning models Noah Gift and Alfredo Deza |
| title_fullStr | Practical MLOps operationalizing machine learning models Noah Gift and Alfredo Deza |
| title_full_unstemmed | Practical MLOps operationalizing machine learning models Noah Gift and Alfredo Deza |
| title_short | Practical MLOps |
| title_sort | practical mlops operationalizing machine learning models |
| title_sub | operationalizing machine learning models |
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