Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence

Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence
Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence by Thomas K Abraham
English | 2018 | ISBN: 1789131956 | 340 Pages | EPUB | 29 MB

Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies
Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way.
The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure.
By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.
What you will learn

  • Discover the benefits of leveraging the cloud for ML and AI
  • Use Cognitive Services APIs to build intelligent bots
  • Build a model using canned algorithms from Microsoft and deploy it as a web service
  • Deploy virtual machines in AI development scenarios
  • Apply R, Python, SQL Server, and Spark in Azure
  • Build and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlow
  • Implement model retraining in IoT, Streaming, and Blockchain solutions
  • Explore best practices for integrating ML and AI functions with ADLA and logic apps