Running distributed workloads is key to the future of AI. As models become more complex and advanced, distributed workloads will be the only way forward.

Get ahead of the curve, and learn practical hands-on guidance from Kubernetes expert Itay Ariel on how to leverage Kubernetes for distributed workloads. Itay will give an overview of the unique challenges of scaling workloads and show how to leverage Kubernetes to easily scale your ML models and automate the management of workload performance.

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What you’ll learn:

• What to consider before implementing Kubernetes to your ML workloads.

• The main challenges of running distributed workloads on Kubernetes.

• Real world examples and use cases such as PyTorch and Spark.

• Best practices for optimizing ML workloads on Kubernetes.

• How to easily use Kubernetes to scale your ML models.

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