The Framework Way is the Best Way: the pitfalls of MLOps and how to avoid them
May 24, 2022 - Stefan Nica - 9 mins read
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 - 5 mins read
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!
All Continuous, All The Time: Pipeline Deployment Patterns with ZenML
May 11, 2022 - Safoine El Khabich - 12 mins read
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.
What's New in v0.7.2 and v0.7.3: HuggingFace, Weights & Biases, LightGBM, XGBoost, and more!
April 28, 2022 - Hamza Tahir - 5 mins read
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.
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.