Transforming Vanilla PyTorch Code into Production Ready ML Pipeline - Without Selling Your Soul (27 Oct 2022)
Transform quickstart PyTorch code as a ZenML pipeline and add experiment tracking and secrets manager component.
Will they stay or will they go? Building a Customer Loyalty Predictor (27 May 2022)
We built an end-to-end production-grade pipeline using ZenML for a customer churn model that can predict whether a customer will remain engaged with the company or not.
Predicting how a customer will feel about a product before they even ordered it (20 Apr 2022)
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.
How to build a three-pointer prediction pipeline (02 Feb 2022)
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?
Get to know ZenML through examples (06 Jan 2022)
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.
Why you should be using caching in your machine learning pipelines (07 Dec 2021)
Use caches to save time in your training cycles, and potentially to save some money as well!
Taking on the ML pipeline challenge (27 Oct 2021)
Why data scientists need to own their ML workflows in production.
Why ML should be written as pipelines from the get-go (31 Mar 2021)
Eliminate technical debt with iterative, reproducible pipelines.
Avoiding technical debt with ML pipelines (06 Jun 2020)
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 (01 May 2020)
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 (01 Apr 2020)
Use YAML files to help configure pipelines that can run complex deep learning training.