AI/ML: Persistent workspaces for multiple users
The first challenge for an AI/ML practitioner is gathering the necessary data to feed the process. The solution? Advanced planning algorithms that organize data better than humans in far less time.
AI/ML resources
Red Hat Developer roundup: Best of January 2022
Don't miss a thing! Here's a roundup of new articles, tutorials, and more published this month on Red Hat Developer.
How to self-host a Python package index using Pulp
Find out how developer teams use Pulp to maintain and share their own Python package repositories. Examples are based on the Operate First deployment.
Extracting dependencies from Python packages
Explore the challenges involved in extracting metadata and dependency information from Python packages, then learn how Project Thoth works around them.
More machine learning with OpenShift Data Science
Use Red Hat OpenShift Data Science to solve two common use cases for machine learning: Accessing Amazon S3 data and creating TensorFlow models.
Build and extend containerized applications with Project Thoth
Learn how Thoth synthesizes software and hardware requirements to provide intelligent Python package recommendations for developers and data scientists.
Access more data from your Jupyter notebook
In this short how-to video, learn how to use Starburst Galaxy to pull data sources into a Jupyter notebook powered by Red Hat OpenShift Data Science.
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July 18, 2019