Data Natives Big Data Copenhagen Meetup happening June 20

GridGain's Yuriy Babak fields a question at the Data Natives Big Data London Meetup on June 5.Join us in Copenhagen on June 20 for a gathering of the Data Natives Big Data Copenhagen Meetup. GridGain's Yuriy Babak will deliver a talk titled, "Distributed Machine and Deep Learning at Scale with Apache Ignite."

"With most machine learning and deep learning frameworks, it can take hours to move data for ETL, and hours to train models, Yuriy explained. "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." 

"ETL" stands for "extract, transform and load." These three database functions are combined into one tool to pull data out of one database and place it into another database. Extract is the process of reading data from a database. In this stage, the data is collected, often from multiple and different types of sources. Transform is the process of converting the extracted data from its previous form into the form it needs to be in so that it can be placed into another database. Transformation occurs by using rules or lookup tables or by combining the data with other data. Load is the process of writing the data into the target database.

During his talk, attendees will 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.

Yuriy's talk will 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

This is a free event but please RSVP to guarantee your spot because space will be limited.

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