Using Apache Ignite for Continuous Machine and Deep Learning at Scale

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data and to train models. It can also be hard to scale with data sets that are increasingly frequently larger than the capacity of any single server. The size of the data can also make it hard to incrementally test and retrain models in near real-time to improve business results.

In this webinar you will learn how the Apache Ignite® in-memory computing platform addresses these machine learning limitations with distributed model training and execution to provide real-time, continuous learning capabilities. Topics include:

  • Overview of distributed ML/DL including design, implementation, usage patterns, pros and cons
  • Overview of Apache Ignite ML/DL, including prebuilt ML/DL, and how to add your own ML/DL algorithms
  • How to integrate Apache Ignite with Apache Spark to improve the Apache Spark data pipeline throughput
  • How Apache Ignite and TensorFlow can be used together to build distributed DL model training and execution

By the end of this webinar, you will understand how distributed ML/DL works with Apache Ignite and how to get started. You will also understand how Ignite can be used to create a continuous machine learning environment to drive real-time business processes.

Ken Cottrell
Ken Cottrell
Solution Architect at GridGain Systems

I’ve been working with distributed computing tools and platforms for 25 years, in both presales and post sales technical roles. I’ve provided technical advisory and consulting services to customers in areas including object-oriented data modeling, data-driven business process integration, and advanced analytics tools and platforms.

These last few years I’ve been advising architects and developers on the use of big data engineering and Machine Learning tools and processes. My role at Gridgain is as a subject matter expert in the data engineering aspects of distributed Machine Learning.