Cloud Native AI and Machine Learning on AWS Premkumar Rangarajan, David Bounds
- Autorzy:
- Premkumar Rangarajan, David Bounds
- Wydawnictwo:
- BPB Publications
- Ocena:
- Stron:
- 400
- Dostępne formaty:
-
ePubMobi
Zostało Ci
na świąteczne zamówienie
opcje wysyłki »
opcje wysyłki »
Opis
książki
:
Cloud Native AI and Machine Learning on AWS
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)
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)
Szczegóły książki
- ISBN Ebooka:
- 978-93-555-1326-7, 9789355513267
- Data wydania ebooka :
-
2024-12-18
Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna
z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie.
Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
- Język publikacji:
- 1
- Rozmiar pliku ePub:
- 6.1MB
- Rozmiar pliku Mobi:
- 6.1MB
Dzięki opcji "Druk na żądanie" do sprzedaży wracają tytuły Grupy Helion, które cieszyły sie dużym zainteresowaniem, a których nakład został wyprzedany.
Dla naszych Czytelników wydrukowaliśmy dodatkową pulę egzemplarzy w technice druku cyfrowego.
Co powinieneś wiedzieć o usłudze "Druk na żądanie":
- usługa obejmuje tylko widoczną poniżej listę tytułów, którą na bieżąco aktualizujemy;
- cena książki może być wyższa od początkowej ceny detalicznej, co jest spowodowane kosztami druku cyfrowego (wyższymi niż koszty tradycyjnego druku offsetowego). Obowiązująca cena jest zawsze podawana na stronie WWW książki;
- zawartość książki wraz z dodatkami (płyta CD, DVD) odpowiada jej pierwotnemu wydaniu i jest w pełni komplementarna;
- usługa nie obejmuje książek w kolorze.
Masz pytanie o konkretny tytuł? Napisz do nas: sklep@ebookpoint.pl
Proszę wybrać ocenę!
Proszę wpisać opinię!
Książka drukowana
Oceny i opinie klientów: Cloud Native AI and Machine Learning on AWS Premkumar Rangarajan, David Bounds (0)
Weryfikacja opinii następuje na podstawie historii zamowień na koncie Użytkownika umiejszczającego opinię.