“Bring elasticity and innovation to Machine Learning and AI operations
● 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.
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.
Details e-Book Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services (English Edition)
🗸 Author(s): Premkumar Rangarajan & David Bounds
🗸 Title: Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services (English Edition)
🗸 Rating : from 5 stars ( reviews)
🗸 Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
🗸 Supported Devices: Android, iOS, MacOS, PC and Amazon Kindle
✓ Read as many eBooks you want!
✓ Secure Scanned. No Virus Detected
✓ Thousands of eBooks to choose from - Hottest new releases
✓ Click it and Read it! - no waiting to read eBooks, it's instant!
✓ Keep reading your favorite eBooks over and over!
✓ It works anywhere in the world!
✓ No late fees or fixed contracts - cancel anytime!
Readers' opinions about Cloud Native AI and Machine Learning on AWS: Use SageMaker for building ML models, automate MLOps, and take advantage of numerous AWS AI services (English Edition) by Premkumar Rangarajan & David Bounds
The characters in this book felt like old friends, and I was sad to say goodbye to them at the end. It's a testament to the author's talent for creating memorable and relatable personas.
This is a book I'll cherish and recommend to everyone. It touched my soul and made me reflect on life's profound mysteries.
I'm already planning to reread this book. It's one of those stories that you can revisit again and again, discovering new layers of meaning each time.