Red Hat OpenShift Data Science Seeks A More Open Approach to Machine Learning Development
Linux is encouraging more people to build ML models on the OpenShift platform by launching the new service today. Red Hat OpenShift is a Kubernetes platform that empowers companies to host modern application components. OpenShift enables businesses to build, modify, and deploy applications on-demand, enabling rapid software development and lifecycle release. Managing large software container clusters Red Hat OpenShift Data Science is a fully managed cloud service for OpenShift that provides an enhanced and streamlined machine learning experience on the OpenShift platform.
According to Red Hat, the idea is to provide data scientists with a machine learning platform that is more customizable than traditional alternatives that limit the use of a very specific set of tools. Red Hat OpenShift Data Science boasts an open workflow platform including Jupyter notebooks and common platforms such as PyTorch and Tensorflow, as well as access to certified partner technologies from the Red Hat Marketplace. You can think of it as a fully integrated machine learning model development environment with easy access to a variety of useful third-party tools.
For example, users can access services that optimize and tune the behavior of machine learning models on Intel Corp hardware. Use the Intel OpenVINO for Enterprise toolbox. You can then access the Intel oneAPI AI Analytics Toolkit, which provides a range of tools and platforms needed to extract analytics from models and optimize performance on Intel CPUs.
The company therefore added that data scientists will have unified access to the tools and environments they need to create and deploy models, which will help ensure the high performance of Intel hardware.
Another major launch partner is Nvidia Corp., where GPUs serve as the perfect engine for more demanding AI and machine learning models. Red Hat says integration with Nvidia hardware and services accelerates computing, allowing data scientists to scale computationally expensive neural networks to large, complex architectures without compromising performance. This means that data scientists can reduce the time it takes to train a model by adding more resources with minimal code changes.
Seldon Deploy Other integrations include the popular Anaconda Inc. IBM Corp.'s Watson Studio AutoAI makes it easy to create, run, and manage AI models at any scale. Seldon Technologies Ltd. Tool which data scientists can use to improve their ML deployment models. and Starburst Data, Inc. platform.
For data analysis. Mike Piech, vice president and general manager of cloud data services at Red Hat, said, "The biggest obstacle to adoption for many enterprises is the difficulty of connecting the data sources they need using a variety of training and model deployment techniques." Through this, we are helping organizations overcome this challenge and unlock the full potential of machine learning from the forerunners of trusted open source technologies." As it is one of the use cases, he said that the new service could attract more enterprises
“The key is that speed is very important for enterprises looking to build the next generation of AI and machine learning-based applications, so the vendor can provide as much turnkey solutions as possible, and “This is what Red Hat understands right now, not only in software, but in our current offerings, which include many useful partner products,” he said. and as an on-site trial add-on to Red Hat OpenShift Service on. This release includes support for general availability. This means that users can test the service by paying only for the underlying infrastructure the service runs on. Amazon Web Services