Deploy your hybrid cloud for ML and DL
OpenShift for ML over IBM Power Systems is the ideal solution for you to reduce costs, modernize your deployments, accelerate your ML/DL workouts, and increase collaboration between your teams. Thanks to hardware specifically designed for these new workloads, the possibilities offered by new GPUs virtualization technologies and the market-leading kubernet-based hybrid cloud solution from Red Hat and IBM.
What is Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
Artificial Intelligence (AI)
Machine Learning (ML)
ML is, within AI, the ability to learn, using different models, without being programmed directly for it. Algorithms and statistical systems such as patterns and inferences are used to achieve certain unattended learning capabilities.
Deep Learning (DL)
What does OpenShift bring to DL and ML systems?
By using containers within our hybrid cloud to deploy our Deep Learning and Machine Learning workloads, we can take much better advantage of infrastructure investment—storage, servers, and networking. Since OpenShift version 4.7, if deployed to Power Systems (specifically on AC922 and IC922models) allows you to run different ML and DL models by sharing even GPUs. This represents a real revolution in on-premises ML projects and more than significant cost savings compared to existing cloud alternatives: think that in addition to the execution costs of the different trainings, you have to upload and download all the data from the cloud with the high costs involved.
Can I still use AWS, Azure, or Google Cloud for ML?
Of course. There will be certain models, workloads, or projects that are interesting for a variety of reasons to use cloud provider services. In others, either because of its high costs or requirements arising from data protection, we will choose to make it our own infrastructure. OpenShift allows you to manage it in a simple and transparent way.
OpenShift Container Storage and DevOps
With the help of OpenShift Container Storage (OCS), each developer can manage different instances and versions of the same model following devops practices and on our own storage. When the model is ready to be deployed, the user can start a configuration and deployment process at any time. A version control system and advanced orchestration capabilities are available including automatic testing of the new code. This is made possible by the latest advances in GPUs virtualization technologies and integration into the only HW platform that has dedicated connections between GPUs (FPGAs) and sockets. This avoids bottlenecks with bandwidth several times that of Intel processor-based architectures. We can also run different models simultaneously on GPUs (FPGAs) of the same graphics card.
Collaboration between data scientists and developers
OpenShift is a unified platform where data scientists, software developers, and system administrators can collaborate in a simple and robust way. This allows you to accelerate the deployment of applications of all kinds, including ML/IA in minutes thanks to its self-service portal. Quickly create, scale, reproduce, test, and share the results of AI/DL/ML models in an agile manner with other people involved in such projects, including project manager, mathematicians, programmers, and clients.
OpenShift for ML Success Stories
Here are some of Red Hat Openshift’s latest success stories for machine learning solutions.
