The machine learning solutions architect handbook: create machine learning platforms to run solutions in an enterprise setting
"When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing
2022
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you'll need to become one. You'll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You'll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. And finally, you'll get acquainted with AWS AI services and their applications in real-world use cases"--Amazon.ca |
| Beschreibung: | Print on demand edition |
| Beschreibung: | xviii, 420 Seiten Illustrationen, Diagramme 10 cm |
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Datensatz im Suchindex
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| adam_text | Table of Contents Preface Section 1: Solving Business Challenges with Machine Learning Solution Architecture 1 Machine Learning and Machine Learning Solutions Architecture What are Al and ML? 4 Business metric tracking 15 Supervised ML Unsupervised ML Reinforcement learning 5 6 8 ML challenges ML solutions architecture 16 16 ML versus traditional software ML life cycle Business understanding and ML problem framing Data understanding and data preparation Model training and evaluation Model deployment Model monitoring 10 11 13 14 14 15 15 Business understanding and ML transformation Identification and verification of ML techniques System architecture design and implementation ML platform workflow automation Security and compliance 19 20 20 Testing your knowledge Summary 21 22 18 18 2 Business Use Cases for Machine Learning ML use cases ¡Ո financial services24 Capital markets front office 24 Capital markets back office operations Risk management and fraud 28 31
vi Table of Contents Insurance 35 ML use cases in media and entertainment 37 Content development and production Content management and discovery Content distribution and customer engagement 38 38 39 ML use cases in healthcare and life sciences 40 Medical imaging analysis Drug discovery Healthcare data management 40 41 43 ML use cases in manufacturing 44 Engineering and product design Manufacturing operations - product quality and yield Manufacturing operations - machine maintenance 44 ML use cases in retail 46 Product search and discovery Target marketing Sentiment analysis Product demand forecasting 46 47 48 49 ML use case identification exercise Summary 50 50 45 46 Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning 3 Machine Learning Algorithms Technical requirements How machines learn Overview of ML algorithms 54 54 56 Consideration for choosing ML algorithms 56 Algorithms for classification and regression problems 58 Algorithms for time series analysis 67 Algorithms for recommendation 69 Algorithms for computer vision problems71 Algorithms for natural language processing problems 73 Generative model 81 Hands-on exercise 83 Problem statement Dataset description Setting up a Jupyter Notebook environment Running the exercise 83 83 Summary 91 83 86
Table of Contents vii 4 Data Management for Machine Learning 94 Technical requirements Data management 94 considerations for ML Data management architecture 96 for ML Data storage and management Data ingestion Data cataloging Data processing Data versioning ML feature store Data serving for client consumption Authentication and authorization Data governance 98 100 103 104 105 106 107 108 109 Hands-on exercise - data management for ML Creating a data lake using Lake Formation Creating a data ingestion pipeline Creating a Glue catalog Discovering and querying data in the data lake Creating an Amazon Glue ETL job to process data for ML Building a data pipeline using Glue workflows Summary 111 112 113 115 116 118 121 123 5 Open Source Machine Learning Libraries Technical requirements 126 Core features of open source machine learning libraries 126 Understanding the scikit-learn machine learning library 127 Installing scikit-learn Core components of scikit-learn Understanding the Apache Spark ML machine learning library Installing Spark ML Core components of the Spark ML library 128 128 130 132 132 Understanding the TensorFlow deep learning library 135 Installing Tensorflow Core components of TensorFlow Hands-on exercise - training a TensorFlow model Understanding the PyTorch deep learning library Installing PyTorch Core components of PyTorch Hands-on exercise - building and training a PyTorch model Summary 137 138 140 143 143 144 146 149
viii Table of Contents 6 Kübemetes Container Orchestration Infrastructure Management Technical requirements Introduction to containers Kübemetes overview and core concepts Networking on Kübemetes Service mesh Security and access management Network security 152 152 Authentication and authorization to APIs Running ML workloads on Kübemetes 154 161 Hands-on - creating a Kübemetes infrastructure on AWS 166 167 168 Problem statement Lab instruction Summary 168 173 174 174 174 180 Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms 7 Open Source Machine Learning Platforms Technical requirements Core components of an ML platform Open source technologies for building ML platforms Using Kubeflow for data science environments Building a model training environment Registering models with a model registry Serving models using model serving services 184 Automating ML pipeline workflows 184 Hands-on exercise - building a data science architecture using 205 open source technologies 185 201 186 189 205 Part 1 - Installing Kubeflow Part 2 - tracking experiments and 211 models, and deploying models Part 3 - Automating with an ML pipeline220 192 Summary 193 232
Table of Contents ix 8 Building a Data Science Environment Using AWS ML Services Technical requirements Data science environment architecture using SageMaker SageMaker Studio SageMaker Processing SageMaker Training Service SageMaker Tuning SageMaker Experiments SageMaker Hosting 234 234 236 238 239 240 241 241 Hands-on exercise - building a data science environment using AWS services 242 Problem statement Dataset Lab instructions Summary J 242 242 242 258 9 Building an Enterprise ML Architecture with AWS ML Services Technical requirements Key requirements for an enterprise ML platform Enterprise ML architecture pattern overview Model training environment Model training engine Automation support Model training life cycle management Model hosting environment deep dive Inference engine Authentication and security control 26՛0 26ı° 26:2 26՛4 26 5 7 26 26 g 26í3 26 ® 27 Monitoring and logging Adopting MLOps for ML workflows 274 274 Components of the MLOps architecture 275 Monitoring and logging 279 Hands-on exercise - building an MLOps pipeline on AWS 289 Creating a CloudFormation template for the ML training pipeline Creating a CloudFormation template for the ML deployment pipeline Summary 289 295 299
x Table of Contents 10 Advanced ML Engineering Technical requirements Training large-scale models with distributed training Distributed model training using data parallelism Distributed model training using model parallelism Achieving low latency model inference How model inference works and opportunities for optimization Hardware acceleration 302 302 303 309 318 318 319 Model optimization Graph and operator optimization Model compilers Inference engine optimization 322 324 326 328 Hands-on lab - running distributed model training with 329 PyTorch Modifying the training script Modifying and running the launcher notebook Summary 329 331 332 11 ML Governance, Bias, Explainability, and Privacy Technical requirements What is ML governance and why is it needed? The regulatory landscape around model risk management Common causes of ML model risks Understanding the ML governance framework Understanding ML bias and explainability Bias detection and mitigation ML explainability techniques Designing an ML platform for governance Data and model documentation 334 334 335 336 337 338 339 340 342 343 Model inventory Model monitoring Change management control Lineage and reproducibility Observability and auditing Security and privacy-preserving ML Hands-on lab - detecting bias, model explainability, and training privacy-preserving models Overview of the scenario Detecting bias in the training dataset Explaining feature importance for the trained model Training privacy-preserving models 345 345 346 347 347 348 353 353 353 358 359
Table of Contents xi 12 Building ML Solutions with AWS Al Services Technical requirements What are Al services? Overview of AWS Al services 364 364 365 366 368 369 371 372 375 376 Amazon Comprehend Amazon Textract Amazon Rekognition Amazon Transcribe Amazon Personalize Amazon Lex Amazon Kendra Evaluating AWS Al services for ML use cases Building intelligent solutions with Al services 378 379 Automating loan document verification and data extraction 379 Index___________________ Other Books You May Enjoy Media processing and analysis workflow 381 E-commerce product recommendation 383 Customer self-service automation with intelligent search 385 Designingen MLOps architecture for Al services AWS account setup strategy for Al services and MLOps Code promotion across environments Monitoring operational metrics for Al services Hands-on lab - running ML tasks using Al services Summary 386 386 388 388 389 394
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| adam_txt |
Table of Contents Preface Section 1: Solving Business Challenges with Machine Learning Solution Architecture 1 Machine Learning and Machine Learning Solutions Architecture What are Al and ML? 4 Business metric tracking 15 Supervised ML Unsupervised ML Reinforcement learning 5 6 8 ML challenges ML solutions architecture 16 16 ML versus traditional software ML life cycle Business understanding and ML problem framing Data understanding and data preparation Model training and evaluation Model deployment Model monitoring 10 11 13 14 14 15 15 Business understanding and ML transformation Identification and verification of ML techniques System architecture design and implementation ML platform workflow automation Security and compliance 19 20 20 Testing your knowledge Summary 21 22 18 18 2 Business Use Cases for Machine Learning ML use cases ¡Ո financial services24 Capital markets front office 24 Capital markets back office operations Risk management and fraud 28 31
vi Table of Contents Insurance 35 ML use cases in media and entertainment 37 Content development and production Content management and discovery Content distribution and customer engagement 38 38 39 ML use cases in healthcare and life sciences 40 Medical imaging analysis Drug discovery Healthcare data management 40 41 43 ML use cases in manufacturing 44 Engineering and product design Manufacturing operations - product quality and yield Manufacturing operations - machine maintenance 44 ML use cases in retail 46 Product search and discovery Target marketing Sentiment analysis Product demand forecasting 46 47 48 49 ML use case identification exercise Summary 50 50 45 46 Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning 3 Machine Learning Algorithms Technical requirements How machines learn Overview of ML algorithms 54 54 56 Consideration for choosing ML algorithms 56 Algorithms for classification and regression problems 58 Algorithms for time series analysis 67 Algorithms for recommendation 69 Algorithms for computer vision problems71 Algorithms for natural language processing problems 73 Generative model 81 Hands-on exercise 83 Problem statement Dataset description Setting up a Jupyter Notebook environment Running the exercise 83 83 Summary 91 83 86
Table of Contents vii 4 Data Management for Machine Learning 94 Technical requirements Data management 94 considerations for ML Data management architecture 96 for ML Data storage and management Data ingestion Data cataloging Data processing Data versioning ML feature store Data serving for client consumption Authentication and authorization Data governance 98 100 103 104 105 106 107 108 109 Hands-on exercise - data management for ML Creating a data lake using Lake Formation Creating a data ingestion pipeline Creating a Glue catalog Discovering and querying data in the data lake Creating an Amazon Glue ETL job to process data for ML Building a data pipeline using Glue workflows Summary 111 112 113 115 116 118 121 123 5 Open Source Machine Learning Libraries Technical requirements 126 Core features of open source machine learning libraries 126 Understanding the scikit-learn machine learning library 127 Installing scikit-learn Core components of scikit-learn Understanding the Apache Spark ML machine learning library Installing Spark ML Core components of the Spark ML library 128 128 130 132 132 Understanding the TensorFlow deep learning library 135 Installing Tensorflow Core components of TensorFlow Hands-on exercise - training a TensorFlow model Understanding the PyTorch deep learning library Installing PyTorch Core components of PyTorch Hands-on exercise - building and training a PyTorch model Summary 137 138 140 143 143 144 146 149
viii Table of Contents 6 Kübemetes Container Orchestration Infrastructure Management Technical requirements Introduction to containers Kübemetes overview and core concepts Networking on Kübemetes Service mesh Security and access management Network security 152 152 Authentication and authorization to APIs Running ML workloads on Kübemetes 154 161 Hands-on - creating a Kübemetes infrastructure on AWS 166 167 168 Problem statement Lab instruction Summary 168 173 174 174 174 180 Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms 7 Open Source Machine Learning Platforms Technical requirements Core components of an ML platform Open source technologies for building ML platforms Using Kubeflow for data science environments Building a model training environment Registering models with a model registry Serving models using model serving services 184 Automating ML pipeline workflows 184 Hands-on exercise - building a data science architecture using 205 open source technologies 185 201 186 189 205 Part 1 - Installing Kubeflow Part 2 - tracking experiments and 211 models, and deploying models Part 3 - Automating with an ML pipeline220 192 Summary 193 232
Table of Contents ix 8 Building a Data Science Environment Using AWS ML Services Technical requirements Data science environment architecture using SageMaker SageMaker Studio SageMaker Processing SageMaker Training Service SageMaker Tuning SageMaker Experiments SageMaker Hosting 234 234 236 238 239 240 241 241 Hands-on exercise - building a data science environment using AWS services 242 Problem statement Dataset Lab instructions Summary J 242 242 242 258 9 Building an Enterprise ML Architecture with AWS ML Services Technical requirements Key requirements for an enterprise ML platform Enterprise ML architecture pattern overview Model training environment Model training engine Automation support Model training life cycle management Model hosting environment deep dive Inference engine Authentication and security control 26՛0 26ı° 26:2 26՛4 26 5 7 26 26 g 26í3 26'® 27 Monitoring and logging Adopting MLOps for ML workflows 274 274 Components of the MLOps architecture 275 Monitoring and logging 279 Hands-on exercise - building an MLOps pipeline on AWS 289 Creating a CloudFormation template for the ML training pipeline Creating a CloudFormation template for the ML deployment pipeline Summary 289 295 299
x Table of Contents 10 Advanced ML Engineering Technical requirements Training large-scale models with distributed training Distributed model training using data parallelism Distributed model training using model parallelism Achieving low latency model inference How model inference works and opportunities for optimization Hardware acceleration 302 302 303 309 318 318 319 Model optimization Graph and operator optimization Model compilers Inference engine optimization 322 324 326 328 Hands-on lab - running distributed model training with 329 PyTorch Modifying the training script Modifying and running the launcher notebook Summary 329 331 332 11 ML Governance, Bias, Explainability, and Privacy Technical requirements What is ML governance and why is it needed? The regulatory landscape around model risk management Common causes of ML model risks Understanding the ML governance framework Understanding ML bias and explainability Bias detection and mitigation ML explainability techniques Designing an ML platform for governance Data and model documentation 334 334 335 336 337 338 339 340 342 343 Model inventory Model monitoring Change management control Lineage and reproducibility Observability and auditing Security and privacy-preserving ML Hands-on lab - detecting bias, model explainability, and training privacy-preserving models Overview of the scenario Detecting bias in the training dataset Explaining feature importance for the trained model Training privacy-preserving models 345 345 346 347 347 348 353 353 353 358 359
Table of Contents xi 12 Building ML Solutions with AWS Al Services Technical requirements What are Al services? Overview of AWS Al services 364 364 365 366 368 369 371 372 375 376 Amazon Comprehend Amazon Textract Amazon Rekognition Amazon Transcribe Amazon Personalize Amazon Lex Amazon Kendra Evaluating AWS Al services for ML use cases Building intelligent solutions with Al services 378 379 Automating loan document verification and data extraction 379 Index_ Other Books You May Enjoy Media processing and analysis workflow 381 E-commerce product recommendation 383 Customer self-service automation with intelligent search 385 Designingen MLOps architecture for Al services AWS account setup strategy for Al services and MLOps Code promotion across environments Monitoring operational metrics for Al services Hands-on lab - running ML tasks using Al services Summary 386 386 388 388 389 394 |
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| spelling | Ping, David ca. 20./21. Jh. Verfasser (DE-588)1277032793 aut The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting David Ping Birmingham Packt Publishing 2022 xviii, 420 Seiten Illustrationen, Diagramme 10 cm txt rdacontent n rdamedia nc rdacarrier Print on demand edition "When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you'll need to become one. You'll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You'll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. And finally, you'll get acquainted with AWS AI services and their applications in real-world use cases"--Amazon.ca Machine learning Apprentissage automatique SCIENCE / General bisacsh Machine learning fast Softwareplattform (DE-588)4702244-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Softwareplattform (DE-588)4702244-9 s DE-604 ebook version Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033914211&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ping, David ca. 20./21. Jh The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting Machine learning Apprentissage automatique SCIENCE / General bisacsh Machine learning fast Softwareplattform (DE-588)4702244-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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| title | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting |
| title_auth | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting |
| title_exact_search | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting |
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| title_full | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting David Ping |
| title_fullStr | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting David Ping |
| title_full_unstemmed | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting David Ping |
| title_short | The machine learning solutions architect handbook |
| title_sort | the machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting |
| title_sub | create machine learning platforms to run solutions in an enterprise setting |
| topic | Machine learning Apprentissage automatique SCIENCE / General bisacsh Machine learning fast Softwareplattform (DE-588)4702244-9 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Apprentissage automatique SCIENCE / General Softwareplattform Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033914211&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT pingdavid themachinelearningsolutionsarchitecthandbookcreatemachinelearningplatformstorunsolutionsinanenterprisesetting |