Predicting how a customer will feel about a product before they even ordered it
April 20, 2022 - Ayush Singh - 8 mins read
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.
Podcast: Trustworthy ML with Kush Varshney
April 14, 2022 - Alex Strick van Linschoten - 1 min read
This week I spoke with Kush Varshney, author of 'Trustworthy Machine Learning', a fantastic guide and overview of all of the different ways machine learning can go wrong and an optimistic take on how to think about addressing those issues.
What's New in v0.7.1: Fetch data from your feature store and deploy models on Kubernetes
April 11, 2022 - Hamza Tahir - 3 mins read
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.
'It's the data, silly!' How data-centric AI is driving MLOps
April 07, 2022 - Hamza Tahir - 9 mins read
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.
What's New in v0.7.0: 🔡 User Profiles and Secret Storage 🤫
March 28, 2022 - James W. Browne - 4 mins read
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 - 6 mins read
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.
Podcast: Practical Production ML with Emmanuel Ameisen
March 18, 2022 - Alex Strick van Linschoten - 1 min read
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.
What's New in v0.6.3: Run Steps on Sagemaker and AzureML ☁️
March 14, 2022 - Alex Strick van Linschoten - 3 mins read
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 - 16 mins read
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.