Data Science

The AI Lab Recipes repository offers recipes for building and running containerized AI and LLM applications to help developers move quickly from prototype to production.

Learn how to build a containerized bootable operating system to run AI models using image mode for Red Hat Enterprise Linux, then deploy a custom image.

Learn how Intel Graphics Processing Units (GPUs) can enhance the performance of machine learning tasks and pave the way for efficient model serving.

Discover how to use machine learning techniques to analyze context, semantics, and relationships between words and phrases indexed in Elasticsearch.

Discover how event-driven architecture can transform data into valuable business intelligence with intelligent applications using AI/ML.

Walk through the basics of fine-tuning a large language model using Red Hat OpenShift Data Science and HuggingFace Transformers.

Learn why graphics processing units (GPUs) have become the foundation of artificial intelligence and how they are being used.

In this article, you will learn how to perform inference on JPEG images using the gRPC API in OpenVINO Model Server in OpenShift. Model servers play an important role in smoothly bringing models from development to production. Models are served via network endpoints which expose an APIs to run predictions.

Intel AI tools save cloud costs, date scientists' time, and time spent developing models. Learn how the AI Kit can help you.

OpenVINO helps you tackle speech-to-text conversion, a common AI use case. Learn more.

Once you have data, how do you start building a PyTorch model? This learning path shows you how to create a PyTorch model with OpenShift Data Science.

Get a video introduction to Project Thoth's cloud-based Python dependency resolver, then learn how to manage Python dependencies on the Thoth command line.

Data encoding is an important part of data preprocessing. Learn three tried-and-true approaches to data conversions in the Python Pandas library.

Open Source Data Pipelines for Intelligent Applications provides data engineers and scientists insight into how Kubernetes provides a platform for building data platforms that increase an organization’s data agility. 

Learn how to set up a Pulp Python repository and publish and consume Python packages using Pulp on the Red Hat Developer Operate First cloud.

Discover how to resolve Python dependencies by extracting metadata and dependency information, and how Project Thoth helps to streamline the process.

Solve the typical data science problems of accessing Amazon S3 data and creating a TensorFlow model by following two new OpenShift Data Science learning paths.

Discover how Project Thoth solves dependency management issues and vulnerabilities in the Python ecosystem, making the resolution process cloud-based.

Red Hat Developer shares its readers' top picks of articles posted in November 2021, including two Linux updates. See the full recap at the end of this article.

Learn how to better build and extend containerized apps by using Project Thoth to control container image quality and provide more robust runtime environments.

Watch this demo by Red Hat Principal Software Engineer Chris Chase on how to build and deploy an object detection model within an intelligent application.

Pull data without duplicating it into a Jupyter notebook using Starburst Galaxy, a fully managed data platform built on Red Hat OpenShift Data Science.

Get hands-on resources for building machine learning models using Red Hat OpenShift Data Science. Learn how to use NLP, Jupyter notebooks, and more.

An intelligent, distributed caching solution that boosts application performance, provides greater deployment flexibility, and minimizes the overhead of standing up new applications.

Thoth prescriptions resolve dependency conflicts in Python application stacks. Learn how to create and use them to build healthier Python applications.