The Machine Learning Solutions Architect Handbook : Create Machine Learning Platforms to Run Solutions in an Enterprise Setting
Saved in:
| Title: | The Machine Learning Solutions Architect Handbook : Create Machine Learning Platforms to Run Solutions in an Enterprise Setting |
|---|---|
| Description: | Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutionsKey FeaturesExplore different ML tools and frameworks to solve large-scale machine learning challenges in the cloudBuild an efficient data science environment for data exploration, model building, and model trainingLearn how to implement bias detection, privacy, and explainability in ML model developmentBook DescriptionWhen 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. 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. What you will learnApply ML methodologies to solve business problemsDesign a practical enterprise ML platform architectureImplement MLOps for ML workflow automationBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using an AI service and a custom ML modelUse AWS services to detect data and model bias and explain modelsWho this book is forThis book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook. |
| Authors: | David Ping |
| Resource Type: | eBook. |
| Subjects: | Machine learning |
| Categories: | COMPUTERS / Business & Productivity Software / General, COMPUTERS / Machine Theory, COMPUTERS / Data Science / Data Modeling & Design |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf – Type: ebook-epub Text: Availability: 0 |
|---|---|
| Header | DbId: nlebk DbLabel: eBook Collection (EBSCOhost) An: 3125157 RelevancyScore: 1103 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 1103.19409179688 |
| IllustrationInfo | |
| ImageInfo | – Size: thumb Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$3125157$PDF&s=r – Size: medium Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$3125157$PDF&s=d |
| Items | – Name: Title Label: Title Group: Ti Data: The Machine Learning Solutions Architect Handbook : Create Machine Learning Platforms to Run Solutions in an Enterprise Setting – Name: Abstract Label: Description Group: Ab Data: Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutionsKey FeaturesExplore different ML tools and frameworks to solve large-scale machine learning challenges in the cloudBuild an efficient data science environment for data exploration, model building, and model trainingLearn how to implement bias detection, privacy, and explainability in ML model developmentBook DescriptionWhen 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. 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. What you will learnApply ML methodologies to solve business problemsDesign a practical enterprise ML platform architectureImplement MLOps for ML workflow automationBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using an AI service and a custom ML modelUse AWS services to detect data and model bias and explain modelsWho this book is forThis book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22David+Ping%22">David Ping</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Business+%26+Productivity+Software+%2F+General%22">COMPUTERS / Business & Productivity Software / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Machine+Theory%22">COMPUTERS / Machine Theory</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Data+Modeling+%26+Design%22">COMPUTERS / Data Science / Data Modeling & Design</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=3125157 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 006.31 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Machine learning Type: general Titles: – TitleFull: The Machine Learning Solutions Architect Handbook : Create Machine Learning Platforms to Run Solutions in an Enterprise Setting Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: David Ping – PersonEntity: Name: NameFull: David Ping IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2021 – D: 13 M: 09 Type: profile Y: 2022 Identifiers: – Type: isbn-print Value: 9781801072168 – Type: isbn-electronic Value: 9781801070416 Titles: – TitleFull: The Machine Learning Solutions Architect Handbook : Create Machine Learning Platforms to Run Solutions in an Enterprise Setting Type: main |
| ResultId | 1 |
