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.
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.
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.
Podcast: ML Engineering with Ben Wilson
June 8, 2022 - Alex Strick van Linschoten
This week I spoke with Ben Wilson, author of 'Machine Learning Engineering in Action', a jam-backed guide to all the lessons that Ben has learned over his years working to help companies get models out into the world and run them in production.
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.
All Continuous, All The Time: Pipeline Deployment Patterns with ZenML
May 11, 2022 - Safoine El Khabich
Connecting model training pipelines to deploying models in production is seen as a difficult milestone on the way to achieving MLOps 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.
Predicting how a customer will feel about a product before they even ordered it
April 20, 2022 - Ayush Singh
We built an end to end continuous deployment pipeline using ZenML for a customer satisfaction model that uses historical data of the customer predict the review score for the next order or purchase.
'It's the data, silly!' How data-centric AI is driving MLOps
April 07, 2022 - Hamza Tahir
ML practitioners today are embracing data-centric machine learning, because of its substantive effect on MLOps practices. In this article, we take a brief excursion into how data-centric machine learning is fuelling MLOps best practices, and why you should care about this change.
Podcast: Practical Production ML with Emmanuel Ameisen
March 18, 2022 - Alex Strick van Linschoten
This week I spoke with Emmanuel Ameisen, a data scientist and ML engineer currently based at Stripe. Emmanuel also wrote an excellent O'Reilly book called 'Building Machine Learning Powered Applications', a book I find myself often returning to for inspiration and that I was pleased to get the chance to reread in preparation for our discussion.
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.
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.
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.
10 Reasons ZenML ❤️ Evidently AI's Monitoring Tool
January 21, 2022 - Alex Strick van Linschoten
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.
