ZenML Blog

The latest news, opinions and technical guides from ZenML.
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MLOps
9 Mins Read

It's the data, silly!' How data-centric AI is driving MLOps

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.
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ZenML
6 Mins Read

Run your steps on the cloud with Sagemaker, Vertex AI, and AzureML

With ZenML 0.6.3, you can now run your ZenML steps on Sagemaker, Vertex AI, and AzureML! It’s normal to have certain steps that require specific infrastructure (e.g. a GPU-enabled environment) on which to run model training, and Step Operators give you the power to switch out infrastructure for individual steps to support this.
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Tech Startup
16 Mins Read

How we made our integration tests delightful by optimizing the way our GitHub Actions run our test suite

As we outgrew our initial template Github Action workflow, here's the five things we added to our Github Action arsenal to fit our growing needs: Caching, Reusable Workflows, Composite Actions, Comment Triggers and Concurrency Management.
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MLOps
6 Mins Read

Everything you ever wanted to know about MLOps maturity models

An exploration of some frameworks created by Google and Microsoft that can help think through improvements to how machine learning models get developed and deployed in production.
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ZenML
11 Mins Read

How to painlessly deploy your ML models with ZenML

Connecting model training pipelines to deploying models in production is regarded as a difficult milestone on the way to achieving Machine Learning operations 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.
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ZenML
7 Mins Read

Type hints are good for the soul, or how we use mypy at ZenML

A dive into Python type hinting, how implementing them makes your codebase more robust, and some suggestions on how you might approach adding them into a large legacy codebase.
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ZenML
5 Mins Read

ZenML - Why we built it

All the advantages that ZenML will bring you if you choose to use it to productionize your model development workflows.
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ZenML
1 Min Read

What's New in v0.5.4

Release notes for the new version of ZenML.
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Tech Startup
3 Mins Read

How we track our todo comments using GitHub Actions

A programmatic means of ensuring #TODO comments made in code also end up in our Jira ticketing system.
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