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Why ML should be written as pipelines from the get-go

March 31, 2021 - Hamza Tahir - 7 mins read

Eliminate technical debt with iterative, reproducible pipelines.

Spot the difference in ML costs

January 28th, 2021 - Hamza Tahir - 5 mins read

Spot instances are a great option for anyone training machine learning models; they're much cheaper than other on-demand options, albeit with some drawbacks.

Is your Machine Learning Reproducible?

January 19th, 2021 - Hamza Tahir - 5 mins read

Short answer: not really, but it can become better!

A most unusual year

December 26th, 2020 - ZenML Team - 5 mins read

A retrospective of some of the decisions that caused us to pivot from a focused machine learning consultancy to deciding to build an open-source MLOps tool.

ZenML will be open source

November 11th, 2020 - ZenML Team - 4 mins read

An overview of some of the capabilities that ZenML will unlock for our users.

MLOps: Learning from history

November 9th, 2020 - ZenML Team - 6 mins read

MLOps isn't just about new technologies and coding practices. Getting better at productionizing your models also likely requires some institutional and/or organisational shifts.

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 - 6 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.