How to run production ML workflows natively on Kubernetes (29 Jun 2022)
Getting started with distributed ML in the cloud: How to orchestrate ML workflows natively on Amazon Elastic Kubernetes Service (EKS).
Serverless MLOps with Vertex AI (27 Jun 2022)
How ZenML lets you have the best of both worlds, serverless managed infrastructure without the vendor lock in.
Move over Kubeflow, there's a new sheriff in town: Github Actions 🤠 (20 Jun 2022)
This tutorial presents an easy and quick way to use GitHub Actions to run ML pipelines in the cloud. We showcase this functionality using Microsoft's Azure Cloud but you can use any cloud provider you like.
Run your steps on the cloud with Sagemaker, Vertex AI, and AzureML (25 Mar 2022)
With ZenML 0.6.3, you can now run your ZenML steps on Sagemaker, Vertex AI, and AzureML! It’s normal to have certain steps that require specific infrastructure (e.g. a GPU-enabled environment) on which to run model training, and Step Operators give you the power to switch out infrastructure for individual steps to support this.
Spot the difference in ML costs (28 Jan 2021)
Spot instances are a great option for anyone training machine learning models; they're much cheaper than other on-demand options, albeit with some drawbacks.