zenml
  • Home
  • Tags
  • ZenML

ZenML | blog

The latest news, opinions and technical guides from ZenML.

Detecting Fraudulent Financial Transactions with ZenML

December 16th, 2022 - Simon Helmig at Two.inc (Guest post)


A winning entry - 2nd prize winner at Month of MLOps 2022 competition.

How to train and deploy a machine learning model on AWS Sagemaker with ZenML and BentoML

December 14th, 2022 - Safoine El khabich


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

December 05th, 2022 - Hamza Tahir


ZenML 0.23.0 comes with a brand-new experiment tracker flavor - Neptune.ai! We dive deeper in this blog post.

ZenML's Month of MLOps Recap

November 22, 2022 - Hamza Tahir


The ZenML MLOps Competition ran from October 10 to November 11, 2022, and was a wonderful expression of open-source MLOps problem-solving.

ZenML 0.22.0: BentoML Integration and A Revamped Airflow Orchestrator!

November 22nd, 2022 - Dickson Neoh


This release comes with a new BentoML integration and a reworked Airflow orchestrator. We also fixed server-related performance issues and other minor improvements!

Transforming Vanilla PyTorch Code into Production Ready ML Pipeline - Without Selling Your Soul

October 27, 2022 - Felix Altenberger and Dickson Neoh


Transform quickstart PyTorch code as a ZenML pipeline and add experiment tracking and secrets manager component.

ZenML 0.20.0: Our Biggest Release Yet!

October 5th, 2022 - Hamza Tahir


The 0.20.0 release is a seminal release in the history of ZenML. Following ten months of continuous feedback and iteration, we bring you a whole new architecture and redesign of ZenML - and a new dashboard to boot! Collaboration among teams has also been taken to a new level in the new version.

ZenML's Month of MLOps: Competition Announcement

September 26, 2022 - Alex Strick van Linschoten


Join us for a celebration of open-source MLOps, where you get to both express your creativity and solve a problem that is interesting to you! Our MLOps Competition runs from October 10 to November 11, 2022.

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.

Deploy your ML models with KServe and ZenML

August 04, 2022 - Safoine El khabich


How to use ZenML and KServe to deploy serverless ML models in just a few steps.

What's New in v0.12.0: Serverless Inferencing on Kubernetes with KServe

August 02, 2022 - Dickson Neoh Tze How


This release brings KServe integration to ZenML.

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.

How to run production ML workflows natively on Kubernetes

June 29, 2022 - Felix Altenberger


Getting started with distributed ML in the cloud: How to orchestrate ML workflows natively on Amazon Elastic Kubernetes Service (EKS).

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.

Serverless MLOps with Vertex AI

June 27, 2022 - Alexej Penner


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 🀠

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.

What's New in v0.9.0: Everyone Gets an Orchestrator!

June 13, 2022 - Alex Strick van Linschoten


We added two new orchestrators (Github Actions and Vertex AI), an Azure Secrets Manager integration, a Slack integration and a bunch of other smaller changes in the latest release.

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: πŸ”‘ 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.

Everything you ever wanted to know about MLOps maturity models

March 7, 2022 - Alex Strick van Linschoten


An exploration of some frameworks created by Google and Microsoft that can help think through improvements to how machine learning models get developed and deployed in production.

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 β€” working in astronomy β€” 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.

Richify that CLI!

February 28, 2022 - Alex Strick van Linschoten


We recently reworked a number of parts of our CLI interface. Here are some quick wins we implemented along the way that can help you improve how users interact with your CLI via the popular open-source library, rich.

What's New in v0.6.2: ♻️ Continuous Deployment and a fresh CLI πŸ‘©β€πŸ’»

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.

What's New in v0.6.1: Reach for the AWS and Azure Cloud! ☁️

February 7, 2022 - Alex Strick van Linschoten


Release notes for the new version of ZenML. We now support AWS S3 and Azure Blob Storage as artifact stores. You'll also find a lot of smaller improvements and bug fixes in this release.

How to build a three-pointer prediction pipeline

February 2, 2022 - Alexej Penner


We challenged ourselves to put our own tool to the test and set up a few pipelines to answer two questions: Did Steph Curry change the game of basketball? And how many three-pointers will be in the next NBA game?

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

What's New in v0.5.7

January 17, 2022 - Alex Strick van Linschoten


Release notes for the new version of ZenML. ZenML now supports MLFlow for tracking pipelines as experiments and Evidently for detecting drift in your ML pipelines in production.

Get to know ZenML through examples

January 6, 2022 - Alex Strick van Linschoten


ZenML has a treasure-trove of examples available for users to get to know specific features. Using these examples, running them and pulling refreshed versions is easy with our CLI that takes on the heavy work for you.

What's New in v0.5.6

December 23, 2021 - Hamza Tahir


Release notes for the new version of ZenML.

Podcast: Monitoring Your Way to ML Production Nirvana with Danny Leybzon

December 16, 2021 - Alex Strick van Linschoten


We discuss how to monitor models in production, and how it helps you in the long-run.

ZenML - Why we built it

December 14, 2021 - Hamza Tahir & Adam Probst


All the advantages that ZenML will bring you if you choose to use it to productionize your model development workflows.

What's New in v0.5.5

December 13, 2021 - Alexej Penner


Release notes for the new version of ZenML.

Why you should be using caching in your machine learning pipelines

December 7, 2021 - Alex Strick van Linschoten


Use caches to save time in your training cycles, and potentially to save some money as well!

What's New in v0.5.4

December 6, 2021 - Alex Strick van Linschoten


Release notes for the new version of ZenML.

Podcast: Practical MLOps with Noah Gift

December 2, 2021 - Alex Strick van Linschoten


We discuss the role of MLOps in an organization, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.

Lazy Loading Integrations in ZenML

November 26, 2021 - Baris Can Durak


How integrations work under the hood to connect you to the tools you know and love.

What's New in v0.5.3

November 24, 2021 - Alex Strick van Linschoten


Release notes for the new version of ZenML.

Pipeline Conversations: Our New Podcast

November 19, 2021 - Adam Probst


We launched a podcast to have conversations with people working to productionize their machine learning models and to learn from their experience.

Introducing the revamped ZenML 0.5.x

November 16, 2021 - Michael Schuster


We rewrote much of our code base to make it more robust and also to enable new features.

Taking on the ML pipeline challenge

October 27, 2021 - Hamza Tahir


Why data scientists need to own their ML workflows in production.

Why ML should be written as pipelines from the get-go

March 31, 2021 - Hamza Tahir


Eliminate technical debt with iterative, reproducible pipelines.

Spot the difference in ML costs

January 28th, 2021 - Hamza Tahir


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.

Is your Machine Learning Reproducible?

January 19th, 2021 - Hamza Tahir


Short answer: not really, but it can become better!

ZenML will be open source

November 11th, 2020 - ZenML Team


An overview of some of the capabilities that ZenML will unlock for our users.

12 Factors of Reproducible Machine Learning in Production

September 28th, 2020 - ZenML Team


A set of guiding principles to help you better productionize your machine learning models.

Avoiding technical debt with ML pipelines

June 6th, 2020 - Hamza Tahir


Pipelines help you think and act better when it comes to how you execute your machine learning training workflows.

Predicting the winner of a DotA 2 match using distributed deep learning pipelines

May 7th, 2020 - Baris Can Durak


ZenML makes it easy to setup training pipelines that give you all the benefits of cached steps.

Deep Learning on 33,000,000 data points using a few lines of YAML

May 4th, 2020 - Hamza Tahir


Use YAML files to help configure pipelines that can run complex deep learning training.

About ZenML

  • Imprint & Data privacy

Links

  • ZenML Home
  • ZenML Docs
  • ZenML Terms of service

Contact Us

  • info@zenml.io