Developer Week Seattle: Cloud Edition


Apache Ignite: 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's also hard to scale, with data sets increasingly being larger than the capacity of any single server. The size of the data also makes it hard to incrementally test and retrain models in near real-time to improve results.

Learn how Apache Ignite in-memory computing platform addresses these ML limitations with distributed model training and execution, to provide near-real-time, continuous learning capabilities. This discussion will explain how distributed ML/DL works with Apache Ignite, and how to get started. 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 in order 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

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.

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