Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions
Key Features
Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
Build an efficient data science environment for data exploration, model building, and model training
Learn how to implement bias detection, privacy, and explainability in ML model development
Book 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. 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.
What you will learn
Apply ML methodologies to solve business problems
Design a practical enterprise ML platform architecture
Implement MLOps for ML workflow automation
Build an end-to-end data management architecture using AWS
Train large-scale ML models and optimize model inference latency
Create a business application using an AI service and a custom ML model
Use AWS services to detect data and model bias and explain models
Who 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.
Key Features
Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud
Build an efficient data science environment for data exploration, model building, and model training
Learn how to implement bias detection, privacy, and explainability in ML model development
Book 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. 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.
What you will learn
Apply ML methodologies to solve business problems
Design a practical enterprise ML platform architecture
Implement MLOps for ML workflow automation
Build an end-to-end data management architecture using AWS
Train large-scale ML models and optimize model inference latency
Create a business application using an AI service and a custom ML model
Use AWS services to detect data and model bias and explain models
Who 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.
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