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Continuous Machine and Deep Learning at Scale with Apache Ignite Denis Magda

September 13, 2019

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours 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 and GridGain help to address these limitations with model training and execution, and help achieve near-real-time, continuous learning. It will be explained how ML/DL work with Apache Ignite, and how to get started. Topics include:n n— Overview of distributed ML/DL including design, implementation, usage patterns, pros and consn— Overview of Apache Ignite ML/DL, including prebuilt ML/DL, and how to add your own ML/DL algorithmsn— Model execution with Apache Ignite, including how to build models with Apache Spark and deploy them in Igniten— How Apache Ignite and TensorFlow can be used together to build distributed DL model training and execution


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