AI and ML on Red Hat OpenShift: What’s the hype?
Artificial Intelligence and Machine Learning have burst onto the IT scene in recent years, but these technologies are no longer reserved just for futuristic robots! They have entered the realm of being useful to businesses, and everyone is talking about them. But what do these words mean? Our Container Platform Consultant, Jonathan, shares his experience installing Red Hat OpenShift and the AI/ML packages.
Written by Jonathan Gazeley, Container Platform Consultant at Tier 2 Consulting.
Artificial Intelligence (AI) is a general term that describes any system where computers can make decisions based on data, spot patterns and trends, and interact with humans in a way that is more natural for humans. For example, the voice-controlled home assistants which can understand natural language, interpret your question, and come up with an answer.
Machine Learning (ML) is a subset of AI which is related to AI systems “learning” to ingest and interpret data. A common example is the photo recognition technology used in many phones and online photo storage services. An ML model has to be trained before it can be used, so it looks at everyone’s photos and makes some guesses. To begin with, humans need to give it some feedback by accepting or rejecting the suggestions so it can learn. Soon, it will have catalogued enough pictures of cats to know what a cat looks like, and if you show it an unknown photo in the future, it will be able to say “yes, that’s a cat” or “no, that’s not a cat”.
Here’s an example of my own photo library, where I have used TensorFlow to describe each photo.
There is now a rich ecosystem of open source AI/ML software and it can be hard to know which components you need. Fortunately, Red Hat has bundled selected tools into two easy-to-understand packages:
Red Hat OpenShift Data Science is a machine learning platform which includes the tools you need to create, train, deploy and monitor ML models.
Red Hat OpenShift AI includes OpenShift Data Science and builds on it with tools to manage the full lifecycle of your AI and ML models.
Both of these packages run on Red Hat OpenShift Container Platform, which means you get all of the usual goodies built in, like monitoring & observability, pipelines/workflows and GitOps, so you can easily integrate your AI/ML components into your existing workflow. The AI/ML packages are deployed by Operators, so they are easy to deploy, and self-managing.
High performance computing in the cloud can be extremely expensive, but OpenShift’s hybrid capabilities give you huge flexibility and cost savings, especially if you have a requirement for specialised hardware – such as GPU accelerated nodes for your AI workloads. OpenShift supports deployment in public cloud providers, on your local virtualisation platform, on physical servers, or on a mixture of these as a hybrid platform.
OpenShift Container Platform also supports OpenShift Data Foundation, which provides fast, software-defined storage for low-latency access by your AI applications.
How can Tier 2 help with AI and ML?
Tier 2 provides a team of OpenShift Consultants with accredited Red Hat OpenShift Container Platform skills to help organisations exploit containers, and agile and DevOps processes, to modernise their applications, develop new cloud-native applications, and accelerate application delivery. This includes the add-on products like OpenShift AI.
If you’re thinking of using OpenShift to run your ML or AI platform, and would like to talk to a Red Hat Container Platform Specialist, please get in touch.
