Graph Powered Machine Learning
Graph Powered Machine Learning


Machine Learning and Graph Processing (e.g., Knowledge Graphs) have been two of the main trends over the past years.
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 we consider data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines.
In this tutorial we will consider the symbiosis of graphs and Machine Learning including the following topics:

*Graph-based Feature Engineering and Graph Algorithms*

We will start by looking at popular graph algorithm and their value for feature engineering for Machine Learning models.

*Graph Embeddings and Graph Neural Networks*

Utilizing graphs as input to Neural Networks is a very powerful combination. In this part, we will consider different Embedding strategies and the field of Graph Neural Networks.

*Graph-based Machine Learning Metadata*

We know about the value of high quality and quantity for building high-quality machine learning models, but for operating a production-grade machine learning pipeline Metadata is equally important. In this part, we will look at leveraging graphs to capture metadata and provenance information of our machine learning ecosystem.

● Jupyter/Colab notebook;
● Hosted Databases;
● Machine Learning Frameworks;


Jörg Schad is Head of Development and Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He is a frequent speaker at meetups, international conferences, and lecture halls.

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