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 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.
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)
Premkumar Rangarajan is a Principal AI/ML specialist solutions architect at Amazon Web Services and has previously authored the book Natural Language Processing with AWS AI services. He has 26 years of experience in the IT industry in a variety of roles, including delivery lead, integration specialist, and enterprise architect. He helps enterprises of all sizes adopt AI and ML, and in his spare time dabbles as a guest lecturer helping students shape their careers.
David Bounds is a Senior Solutions Architect at Amazon Web Services based out of Atlanta, Georgia. With more than 25 years in IT ranging from front-line support to Site Reliability Engineering, David has a focus on operationalization of workloads. He has an unrelenting passion for bringing AI/ML tools and capabilities to engineers of all experience levels and backgrounds. When he is not iterating over haiku-generation models, he runs, works on his 3-D printer, or watches movies with his boxer, Darby. He is preferential to action movies.
