Название: Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI servicesАвтор: Premkumar Rangarajan, David Bounds
Издательство: BPB Publications
Год: 2023
Страниц: 589
Язык: английский
Формат: epub (true)
Размер: 10.2 MB
Bring elasticity and innovation to Machine Learning and AI operations.
Key Features
- Coverage includes a wide range of AWS AI and ML services to help you speedily get fully operational with ML.
- Packed with real-world examples, practical guides, and expert data science methods for improving AI/ML education on AWS.
- Includes ready-made, purpose-built models as AI services and proven methods to adopt MLOps techniques.
Description
Using Machine Learning (ML) and Artificial Intelligence (AI) in existing business processes has been successful. Even AWS's ML and AI services make it simple and economical to conduct machine learning experiments. This book will show readers how to use the complete set of AI and ML services available on AWS to streamline the management of their whole AI operation and speed up their innovation.
In this book, you'll learn how to build data lakes, build and train machine learning models, automate MLOps, ensure maximum data reusability and reproducibility, and much more. The applications presented in the book show how to make the most of several different AWS offerings, including Amazon Comprehend, Amazon Rekognition, Amazon Lookout, and AutoML. This book teaches you to manage massive data lakes, train artificial intelligence models, release these applications into production, and track their progress in real-time. You will learn how to use the pre-trained models for various tasks, including picture recognition, automated data extraction, image/video detection, and anomaly detection. Every step of your Machine Learning and AI project's development process is optimised throughout the book by utilising Amazon's pre-made, purpose-built AI services.
This book will present the readers with how data developers, data scientists, and cloud engineers can seamlessly drive the entire ML and AI on AWS, making maximum use of various AWS machine learning and AI services. In this book, we will create data lakes, prepare and train ML models, automate MLOps, prepare for maximum data reusability and reproducibility, and various other tasks of successful AI deployments.
The book covers use-cases demonstrating effective use of AWS AI/ML services. Readers will learn to leverage massive scale computing, manage large data lakes, train ML and AI models, deploy them into production, and monitor the performance of ML applications. The book also covers how readers can use the pre-trained models across various applications such as image recognition, automated data extraction, detection of images/videos, identifying anomalies, and more. Throughout the book, we use AWS capabilities such as Amazon Sagemaker, Amazon AI Services, frameworks such as Pytorch or TF.
What you will learn
- Learn how to build, deploy, and manage large-scale AI and ML applications on AWS.
- Get your hands dirty with AWS AI services like SageMaker, Comprehend, Rekognition, Lookout, and AutoML.
- Master data transformation, feature engineering, and model training with Amazon SageMaker modules.
- Use neural networks, distributed learning, and deep learning algorithms to improve ML models.
- Use AutoML, SageMaker Canvas, and Autopilot for Model Deployment and Evaluation.
- Acquire expertise with Amazon SageMaker Studio, Jupyter Server, and ML frameworks such as TensorFlow and MXNet.
Who this book is for
Data Engineers, Data Scientists, AWS and Cloud Professionals who are comfortable with Machine Learning and the fundamentals of Python will find this book powerful. Familiarity with AWS would be helpful but is not required.
Table of Contents
1. Introducing the ML Workflow
2. Hydrating the Data Lake
3. Predicting the Future With Features
4. Orchestrating the Data Continuum
5. Casting a Deeper Net (Algorithms and Neural Networks)
6. Iteration Makes Intelligence (Model Training and Tuning)
7. Let George Take Over (AutoML in Action)
8. Blue or Green (Model Deployment Strategies)
9. Wisdom at Scale with Elastic Inference
10. Adding Intelligence with Sensory Cognition
11. AI for Industrial Automation
12. Operationalized Model Assembly (MLOps and Best Practices)
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