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.
Solving Atari Games with Reinforcement Learning (AI) (17 Mar 2022)
We trained a model to solve Atari games using reinforcement learning. We used the Deep Q algorithm as the basis of our implementation. It allowed us to get a working solution fairly quickly.
How to improve your experimentation workflows with MLflow Tracking and ZenML (09 Feb 2022)
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.
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.
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.