I think of machine learning as tools and technologies that help us find meaning in data. In this article, we’ll look at how understanding data helps us build better models.
Continue reading The machine learning life cycle, Part 1: Methods for understanding data
This article is the first in a two-part article series on Kubernetes configuration patterns, which represent ways of configuring Kubernetes applications and controllers. Part 1 introduces simple approaches that use only Kubernetes primitives. These patterns are applicable to any application running on Kubernetes. Part 2 will introduce more advanced patterns. These patterns require you to code against the Kubernetes API when you are developing Kubernetes controllers.
Continue reading Kubernetes configuration patterns, Part 1: Patterns for Kubernetes primitives
Red Hat OpenShift Data Science is a managed cloud service built from a curated set of components from the upstream Open Data Hub project. It aims to provide a stable sandbox in which data scientists can develop, train, and test their machine learning (ML) workloads and then deploy results in a container-ready format. This article summarizes the advantages of using OpenShift Data Science in your machine learning projects.
Continue reading 4 reasons you’ll love using Red Hat OpenShift Data Science
JupyterLab is a flexible and powerful tool for working with Jupyter notebooks. Its interactive user interface (UI) lets you use terminals, text editors, file browsers, and other components alongside your Jupyter notebook. JupyterLab 3.0 was released in January 2021.
Continue reading Managing Python dependencies with the Thoth JupyterLab extension
Red Hat Decision Manager helps organizations introduce the benefits of artificial intelligence to their daily operations. It is based on Drools, a popular open source project known for its powerful rules engine.
Continue reading Knowledge meets machine learning for smarter decisions, Part 2
Drools is a popular open source project known for its powerful rules engine. Few users realize that it can also be a gateway to the amazing possibilities of artificial intelligence. This two-part article introduces you to using Red Hat Decision Manager and its Drools-based rules engine to combine machine learning predictions with deterministic reasoning. In Part 1, we’ll prepare our machine learning logic. In Part 2, you’ll learn how to use the machine learning model from a knowledge service.
Continue reading Knowledge meets machine learning for smarter decisions, Part 1
One of the first tools we developed to help us with Project Thoth was Kebechet, which we named for the goddess of freshness and purification. As we separated our software into more and more repositories (each of our Python modules is in its own repository on GitHub), we needed help with releasing new versions and keeping all dependent modules up-to-date. In a team of two and with more than 35 repositories, our process was a major time-burner.
Continue reading Use Kebechet machine learning to perform source code operations
Project Thoth develops open source tools that enhance the day-to-day life of developers and data scientists. Thoth uses machine-generated knowledge to boost the performance, security, and quality of your applications using artificial intelligence (AI) through reinforcement learning (RL). This machine-learning approach is implemented in Thoth adviser (if you want to know more, click here) and it is used by Thoth integrations to provide the software stack based on user inputs.
Continue reading AI software stack inspection with Thoth and TensorFlow
The new Open Data Hub version 0.8 (ODH) release includes many new features, continuous integration (CI) additions, and documentation updates. For this release, we focused on enhancing JupyterHub image builds, enabling more mixing of Open Data Hub and Kubeflow components, and designing our comprehensive end-to-end continuous integration and continuous deployment and delivery (CI/CD) process. In this article, we introduce the highlights of this newest release.
Note: Open Data Hub is an open source project and a community Operator for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift.
Continue reading “Kubeflow 1.0 monitoring and enhanced JupyterHub builds in Open Data Hub 0.8”
Open Data Hub (ODH) is a blueprint for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift 4. Version 0.7 of Open Data Hub includes support for deploying Kubeflow 1.0 on OpenShift, as well as increased component testing on the OpenShift continuous integration (CI) system. This article explores the recent updates.
Continue reading Open Data Hub 0.7 adds support for Kubeflow 1.0