Join us in Oslo on June 25 for a gathering of the Data Natives Oslo Meetup. GridGain's Yuriy Babak will deliver a talk titled, "Distributed Machine and Deep Learning at Scale with Apache Ignite."

Summary: With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data for ETL, 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 amount of the data also makes it hard to incrementally test and retrain models in near real-time.   

Learn how Apache Ignite and GridGain help to address limitations like ETL costs, scaling issues and Time-To-Market for the new models and help achieve near-real-time, continuous learning. [Yuri Babak, the head of ML/DL framework development at GridGain and Apache Ignite committer,] will explain how ML/DL work with Apache Ignite, and how to get started. Topics include:
 
* Overview of distributed ML/DL including architecture, implementation, usage patterns, pros and cons
* Overview of Apache Ignite ML/DL, including built-in ML/DL algorithmes, and how to implement your own
* Model inference with Apache Ignite, including how to train models with other libraries, like Apache Spark, and deploy them in Ignite
* How Apache Ignite and TensorFlow can be used together to build distributed DL model training and inference