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Practical Automated Machine Learning Using H2O.ai

You're reading from   Practical Automated Machine Learning Using H2O.ai Discover the power of automated machine learning, from experimentation through to deployment to production

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781801074520
Length 396 pages
Edition 1st Edition
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Author (1):
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Salil Ajgaonkar Salil Ajgaonkar
Author Profile Icon Salil Ajgaonkar
Salil Ajgaonkar
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Table of Contents (19) Chapters Close

Preface 1. Part 1 H2O AutoML Basics
2. Chapter 1: Understanding H2O AutoML Basics FREE CHAPTER 3. Chapter 2: Working with H2O Flow (H2O’s Web UI) 4. Part 2 H2O AutoML Deep Dive
5. Chapter 3: Understanding Data Processing 6. Chapter 4: Understanding H2O AutoML Architecture and Training 7. Chapter 5: Understanding AutoML Algorithms 8. Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics 9. Chapter 7: Working with Model Explainability 10. Part 3 H2O AutoML Advanced Implementation and Productization
11. Chapter 8: Exploring Optional Parameters for H2O AutoML 12. Chapter 9: Exploring Miscellaneous Features in H2O AutoML 13. Chapter 10: Working with Plain Old Java Objects (POJOs) 14. Chapter 11: Working with Model Object, Optimized (MOJO) 15. Chapter 12: Working with H2O AutoML and Apache Spark 16. Chapter 13: Using H2O AutoML with Other Technologies 17. Index 18. Other Books You May Enjoy

Using H2O AutoML and Apache Storm

Apache Storm is an open source data analysis and computation tool for processing large amounts of stream data in real time. In the real world, you will often have plenty of systems that continuously generate large amounts of data. You may need to make some computations or run some processes on this data to extract useful information as it is generated in real time.

What is Apache Storm?

Let’s take the example of a log system in a very heavily used web service. Assuming that this web service receives millions of requests per second, it is going to generate tons of logs. And you already have a system in place that stores these logs in your database. Now, this log data will eventually pile up and you will have petabytes of log data stored in your database. Querying all this historical data to process it in one go is going to be very slow and time-consuming.

What you can do is process the data as it is generated. This is where Apache Storm...

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