How to train and deploy a machine learning model on AWS Sagemaker with ZenML and BentoML (14 Dec 2022)
Learn how to use ZenML pipelines and BentoML to easily deploy machine learning models, be it on local or cloud environments. We will show you how to train a model using ZenML, package it with BentoML, and deploy it to a local machine or cloud provider. By the end of this post, you will have a better understanding of how to streamline the deployment of your machine learning models using ZenML and BentoML.
Tracking experiments in your MLOps pipelines with ZenML and Neptune (05 Dec 2022)
ZenML 0.23.0 comes with a brand-new experiment tracker flavor - Neptune.ai! We dive deeper in this blog post.
Transforming Vanilla PyTorch Code into Production Ready ML Pipeline - Without Selling Your Soul (27 Oct 2022)
Transform quickstart PyTorch code as a ZenML pipeline and add experiment tracking and secrets manager component.
Keep the lint out of your ML pipelines! Use Deepchecks to build and maintain better models with ZenML! (06 Sep 2022)
Test automation is tedious enough with traditional software engineering, but machine learning complexities can make it even less appealing. Using Deepchecks with ZenML pipelines can get you started as quickly as it takes you to read this article.
Deploy your ML models with KServe and ZenML (04 Aug 2022)
How to use ZenML and KServe to deploy serverless ML models in just a few steps.
ZenML sets up Great Expectations for continuous data validation in your ML pipelines (07 Jul 2022)
ZenML combines forces with Great Expectations to add data validation to the list of continuous processes automated with MLOps. Discover why data validation is an important part of MLOps and try the new integration with a hands-on tutorial.
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.
How to improve your experimentation workflows with MLflow Tracking and ZenML (09 Feb 2022)
Use MLflow Tracking to automatically ensure that you're capturing data, metadata and hyperparameters that contribute to how you are training your models. Use the UI interface to compare experiments, and let ZenML handle the boring setup details.
10 Reasons ZenML ❤️ Evidently AI's Monitoring Tool (21 Jan 2022)
ZenML recently added an integration with Evidently, an open-source tool that allows you to monitor your data for drift (among other things). This post showcases the integration alongside some of the other parts of Evidently that we like.