Workshop Stage 1 - Hopin 13
Wednesday, September 30, 2020
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