Book description
Abstract
As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments.
In this IBM® Redbooks® publication, we cover the best practices
for deploying and integrating some of the best AI solutions on the
market, including:
IBM Watson Machine Learning Accelerator (see note for product
naming)
IBM Watson Studio Local
IBM Power Systems™
IBM Spectrum™ Scale
IBM Data Science Experience (IBM DSX)
IBM Elastic Storage™ Server
Hortonworks Data Platform (HDP)
Hortonworks DataFlow (HDF)
H2O Driverless AI
We map out all the integrations that are possible with our
different AI solutions and how they can integrate with your
existing or new data lake. We also walk you through some of our
client use cases and show you how some of the industry leaders are
using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to
drive decision making. We also advise you on your deployment
options, when to use a GPU, and why you should use the IBM Elastic
Storage Server (IBM ESS) to improve storage management. Lastly, we
describe how to integrate IBM Watson Machine Learning Accelerator
and Hortonworks with or without IBM Watson Studio Local, how to
access real-time data, and security.
Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise.
Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.
Table of contents
- Front cover
- Figures
- Tables
- Examples
- Notices
- Preface
- Chapter 1. Solution overview
-
Chapter 2. Integration overview
- 2.1 Architecture overview
- 2.2 System configurations
-
2.3 Deployment options
- 2.3.1 Deploying IBM Watson Studio Local in stand-alone mode or with IBM Watson Machine Learning Accelerator
- 2.3.2 Using the Hadoop Integration service versus using an Apache Livy connector
- 2.3.3 Deploying H2O Driverless AI in stand-alone mode or within IBM Watson Machine Learning Accelerator
- 2.3.4 Running Spark jobs
- 2.4 IBM Watson Machine Learning Accelerator and Hortonworks Data Platform
- 2.5 IBM Watson Studio Local with Hortonworks Data Platform
- 2.6 IBM Watson Studio Local with IBM Watson Machine Learning Accelerator
- 2.7 IBM Spectrum Scale and Hadoop Integration
- 2.8 Security
- Chapter 3. Integrating new data
- Chapter 4. Integration details
- Chapter 5. Accessing real-time data
- Appendix A. Additional information
- Appendix B. Installing an IBM Watson Machine Learning Accelerator notebook
- Related publications
- Back cover
Product information
- Title: AI and Big Data on IBM Power Systems Servers
- Author(s):
- Release date: March 2019
- Publisher(s): IBM Redbooks
- ISBN: 9780738457512
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