Page 9 of 9 for Blog - page 9 | ZenML Blog

12 Factors of Reproducible Machine Learning in Production

September 28th, 2020 - ZenML Team - 10 mins read

A set of guiding principles to help you better productionize your machine learning models.

Why ML in production is (still) broken - [#MLOps2020]

June 26th, 2020 - Hamza Tahir - 5 mins read

The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.

Can you do the splits?

June 11th, 2020 - Hamza Tahir - 7 mins read

Splitting up datasets is part of the daily work of a data scientist, but there's more complexity and art to it than first meets the eye.

Avoiding technical debt with ML pipelines

June 6th, 2020 - Hamza Tahir - 10 mins read

Pipelines help you think and act better when it comes to how you execute your machine learning training workflows.

A case for declarative configurations for ML training

May 17th, 2020 - ZenML Team - 5 mins read

Using config files to specify infrastructure for training isn't widely practiced in the machine learning community, but it helps a lot with reproducibility.

Predicting the winner of a DotA 2 match using distributed deep learning pipelines

May 7th, 2020 - Baris Can Durak - 12 mins read

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

May 4th, 2020 - Hamza Tahir - 11 mins read

Use YAML files to help configure pipelines that can run complex deep learning training.

Why deep learning development in production is (still) broken

May 1st, 2020 - Hamza Tahir - 3 mins read

Software engineering best practices have not been brought into the machine learning space, with the side-effect that there is a great deal of technical debt in these code bases.

Distributed PCA using TFX

February 27, 2020 - Hamza Tahir - Crossposted on Tensorflow Blog - 5 mins read

We use PCA to reduce the dimension of input vectors while retaining maximal variance.