Multi-model Powered Machine Learning

This course is a part of Big Data LDN 2019 , a 101-course Paths series from Pluralsight.

With the rapid and recent rise of data science, machine learning frameworks, such as TensorFlow, have become popular. However, those frameworks do not form a complete Machine Learning Platform by themselves. In this talk, Jorg Schad will look at what role databases play in the Machine Learning World, in particular Multi-Model databases supporting multiple data models such as graphs, documents, and key-values. Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. There are even more applications once you consider data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines. But how can you combine Multi-Model Databases with Machine Learning Systems, such as TensorFlow or Pytorch? Using real-world examples, Jorg shows how Multi-Model databases and machine learning frameworks form a very powerful combination. In particular, there will be a focus on graph-based Machine Learning models as well as graph-based data pre-processing and feature engineering (which can, in turn, serve as input for a deep neural network).