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 desig...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt Publishing
2022
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| Schlagworte: | |
| Online-Zugang: | DE-Aug4 DE-M347 DE-706 DE-573 |
| 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. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 online resource (440 pages) |
| Format: | Mode of access: World Wide Web |
| ISBN: | 9781801070416 |
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| 520 | 3 | |a 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. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional | |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:34:24Z |
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| institution | BVB |
| isbn | 9781801070416 |
| language | English |
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| spelling | Ping, David Verfasser aut The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting David Ping Birmingham ; Mumbai Packt Publishing 2022 1 online resource (440 pages) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index 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. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional Mode of access: World Wide Web COMPUTERS / Business & Productivity Software / General COMPUTERS / Machine Theory COMPUTERS / Data Science / Data Modeling & Design Electronic books Erscheint auch als Druck-Ausgabe 978-1-80107-216-8 |
| spellingShingle | Ping, David The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting |
| 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 |
| title_exact_search_txtP | The machine learning solutions architect handbook create machine learning platforms to run solutions in an enterprise setting |
| 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 |
| work_keys_str_mv | AT pingdavid themachinelearningsolutionsarchitecthandbookcreatemachinelearningplatformstorunsolutionsinanenterprisesetting |