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▄ Pluralsight ▄
Multi Model Powered Machine Learning
Date...: 2020-06-08
Type...: Bookware
Disks..: 7x15MB
Notes..: Big Data LDN 2019 | Multi-model
Powered Machine Learning | Jorg Schad
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).
Install: Unpack, Wise up and Enjoy!
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