What's New in v0.13: Spark, Custom Code Deployment, Stack Recipes, and More
September 21, 2022 - Dickson Neoh Tze How
This release blog describes the changes for three releases v0.13.0 (major release), v0.13.1 and v0.13.2 (minor releases). v0.13.0 brings the first iteration of our Apache Spark integration. v0.13.1 and v0.13.2 includes several bugfixes and quality of life improvements for ZenML users.
Keep the lint out of your ML pipelines! Use Deepchecks to build and maintain better models with ZenML!
September 06, 2022 - Stefan Nica
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.
What's New in v0.11.0: Label All The Things!
July 19, 2022 - Alex Strick van Linschoten
This release brings the first iteration of the ZenML annotation stack component and an integration with Label Studio, the popular open-source tool that supports many annotation types. We've also made significant updates to our documentation.
ZenML sets up Great Expectations for continuous data validation in your ML pipelines
July 07, 2022 - Stefan Nica
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.
What's New in v0.10.0: A Kubernetes Native Orchestrator!
June 28, 2022 - Jayesh Sharma
This release brings the highly-requested Kubernetes Orchestrator and a Vault secret manager to ZenML! We have also added Data Validators as a new stack component and an implementation for Great Expectations to kick things off.
Move over Kubeflow, there's a new sheriff in town: Github Actions 🤠
June 20, 2022 - Michael Schuster
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.
The Framework Way is the Best Way: the pitfalls of MLOps and how to avoid them
May 24, 2022 - Stefan Nica
As our AI/ML projects evolve and mature, our processes and tooling also need to keep up with the growing demand for automation, quality and performance. But how can we possibly reconcile our need for flexibility with the overwhelming complexity of a continuously evolving ecosystem of tools and technologies? MLOps frameworks promise to deliver the ideal balance between flexibility, usability and maintainability, but not all MLOps frameworks are created equal. In this post, I take a critical look at what makes an MLOps framework worth using and what you should expect from one.
What's New in v0.8.0: Extend ZenML Any Way You Like!
May 18, 2022 - Alex Strick van Linschoten
Release notes for the new version of ZenML. We've added a ton of extensibility improvements, documentation and guides that take away most of the hard work of figuring out how to add custom components. Our CLI also has been beautified and it should even run a bit faster too!
What's New in v0.7.2 and v0.7.3: HuggingFace, Weights & Biases, LightGBM, XGBoost, and more!
April 28, 2022 - Hamza Tahir
The release adds experiment tracking stack components, integrations with HuggingFace, Weights & Biases and also improvements to how the Seldon Core deployer handles secrets from the secrets manager component.
What's New in v0.7.1: Fetch data from your feature store and deploy models on Kubernetes
April 11, 2022 - Hamza Tahir
The release introduces the Seldon Core ZenML integration, featuring the Seldon Core Model Deployer and a Seldon Core standard model deployer step. It also includes two new integrations with Feast as ZenML's first feature store addition and NeuralProphet adding to the growing list of training libraries supported.
What's New in v0.7.0: 🔡 User Profiles and Secret Storage 🤫
March 28, 2022 - James W. Browne
Release notes for the new version of ZenML. We've added the ability to store your stacks in profiles accessible system-wide instead of just in individual project folders. We also added a way to store and retrieve passwords and secrets, including the possibility to integrate the AWS Secrets Manager. Also: run individual steps on Vertex AI.
Run your steps on the cloud with Sagemaker, Vertex AI, and AzureML
March 25, 2022 - Hamza Tahir
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.
What's New in v0.6.3: Run Steps on Sagemaker and AzureML ☁️
March 14, 2022 - Alex Strick van Linschoten
Release notes for the new version of ZenML. We've added the ability to run steps on AWS Sagemaker and AzureML. We added a new Tensorboard visualization that runs when using the Kubeflow orchestrator. You'll also find a lot of smaller improvements, documentation additions and bug fixes in this release.
How we made our integration tests delightful by optimizing the way our GitHub Actions run our test suite
March 9, 2022 - Alexej Penner
As we outgrew our initial template Github Action workflow, here's the five things we added to our Github Action arsenal to fit our growing needs: Caching, Reusable Workflows, Composite Actions, Comment Triggers and Concurrency Management.
Podcast: From Academia to Industry with Johnny Greco
March 3, 2022 - Alex Strick van Linschoten
This week I spoke with Johnny Greco, a data scientist working at Radiology Partners. Johnny transitioned into his current work from a career as an academic — working in astronomy — where also worked in the open-source space to build a really interesting synthetic image data project.
How to painlessly deploy your ML models with ZenML
March 2, 2022 - Stefan Nica
Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations maturity for an organization. ZenML rises to the challenge and introduces a novel approach to continuous model deployment that renders a smooth transition from experimentation to production.
What's New in v0.6.2: ♻️ Continuous Deployment and a fresh CLI 👩💻
February 23, 2022 - Alex Strick van Linschoten
Release notes for the new version of ZenML. We've added the ability to serve models using MLflow deployments, and we've refreshed how our CLI looks using the popular 'rich' library. You'll also find a lot of smaller improvements, documentation additions and bug fixes in this release.
Podcast: The Modern Data Stack with Tristan Zajonc
February 10, 2022 - Alex Strick van Linschoten
Tristan and Alex discuss where machine learning and AI are headed in terms of the tooling landscape. Tristan outlined a vision of a higher abstraction level, something he's working on making a reality as CEO at Continual.
How to improve your experimentation workflows with MLflow Tracking and ZenML
February 9, 2022 - Alex Strick van Linschoten
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.
Podcast: Neurosymbolic AI with Mohan Mahadevan
January 27, 2022 - Alex Strick van Linschoten
Mohan and Alex discuss neurosymbolic AI and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.
What's New in v0.6.0: whylogs integration and some big core architecture changes
January 26, 2022 - Alex Strick van Linschoten
Release notes for the new version of ZenML. ZenML now supports whylogs (from whylabs) that logs data from your ML pipelines in production. We also made some sizeable (breaking 😢) core architecture changes
