Maximise the impact of AI/ML with enterprise Kubernetes

We’re witnessing an astonishing growth in the volume of information stored and processed in real time - now measured in zetabytes! The world is now well past the point where making sense of all this information is humanly possible and we need specialised tools such as AI/ML to reveal new intelligence and drive actionable outcomes

Now algorithms can tackle more ambitious problems. AI/ML platforms can now be deployed at a fraction of the time and cost, and made use of by a new generation of data scientists and data engineers.

Yet despite these advances, challenges persist:

• Data Scientists and Data Engineers can’t get ready access to the tools and compute power they need to respond to business demand
• A lack of frameworks and architecture to support model building, deployment and monitoring, inherently affects delivery speed of AI/ML workloads
• Siloed teams, manual processes and lengthy process results in increased cost

HANDS-ON WORKSHOP - Tuesday, 22 June @ 10am to 2pm

Our experts will guide you through the entire AI/ML Lifecycle, from Data Engineering, Data Science, and ML-Ops that will see your models running in production.

In this workshop, you will create workflows and get hands-on with:

• Self Service of data engineering and data science focused tools including Apache Kafka, Apache Spark, Jupyter Notebooks
• Collaboration and workflow handoff tools such as shared S3 object storage and model repositories and visualization tools such as Verta.ai and Superset
• Automating and secure model deployment including MLOPs using Jenkins
• Inferencing and model serving tools including Seldon, enabling simple model consumption by intelligent applications over RESTful interfaces.

You’ll experience how Red Hat’s enterprise Kubernetes platform, OpenShift, solves problems that often reduce the business value of AI/ML including:

• How to produce high quality data for training AI models
• Self-service tool provisioning - radically improving productivity
• Kubernetes scheduling improving hardware utilisation
• Elimination of silos - and enabling rapid work handoff between parties
• Safe, easy and fast movement of models to production for inference

Who should attend?

• Managers/Heads of - Data Engineers, Data Scientists and Developers
• Data Engineers, Data Scientists and Developers

While coding / development skills aren’t required, a familiarity with coding will be advantageous.

Questions? Please contact Rebecca Innes, rinnes@redhat.com.