Achieving Continuous Machine and Deep Learning with Apache Ignite and TensorFlow

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.

In this Webinar, Yuri Babak, the head of ML/DL framework development at GridGain and major contributor to Apache Ignite, will explain how ML and DL work with Apache Ignite, and how to get started. Topics include:

• An overview of Apache Ignite ML/DL, including pre-built ML/DL, and how to add your own ML/DL algorithms
• How to use Apache Ignite as a distributed data source for TensorFlow for deep learning
• How to train models, using a TensorFlow cluster on top of Apache Ignite
• How to evaluate models and perform inference of TensorFlow models over Apache Ignite cluster