Machine and Deep Learning with In-Memory Computing

In this talk, Akmal Chaudhri, technology evangelist at GridGain Systems, explains these new capabilities and how to use them in Apache Ignite.

https://www.meetup.com/Deep-Learning-NYC/

This talk provides:

An overview of the ML and DL algorithms and how they work
Examples of how to implement each ML and DL algorithm
Tips and tricks for getting the most performance out of ML and DL.

Next, Akmal unveils GridGain's deep learning capability, which is part of the GridGain Continuous Learning Framework. (GridGain was built upon Apache Ignite). This framework enables real-time model training and can improve models and outcomes as events happen. The deep learning capability is built on the Apache Ignite multilayer perceptron neural network which is included in GridGain. The neural network is optimized for massively parallel processing (MPP), allowing the system to run each algorithm locally against the data residing on each node of the GridGain cluster.

He also demonstrates how models can be continuously retrained as events happen to help improve decisions and outcomes with GridGain deep learning. The GridGain in-memory computing platform supports hybrid transactional/analytical processing (HTAP), allowing any analytics or deep learning to be run in-place in memory within a transaction or interaction.