We discuss the role of MLOps in an organization, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.
A programmatic means of ensuring #TODO comments made in code also end up in our Jira ticketing system.
We launched a podcast to have conversations with people working to productionize their machine learning models and to learn from their experience.
A mix of mental and technical skills you can develop to get better at testing your Python code.
Why data scientists need to own their ML workflows in production.
Eliminate technical debt with iterative, reproducible pipelines.
Short answer: not really, but it can become better!
An overview of some of the capabilities that ZenML will unlock for our users.
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.
A set of guiding principles to help you better productionize your machine learning models.
![Why ML in production is (still) broken - [#MLOps2020]](https://assets.zenml.io/webflow/64a817a2e7e2208272d1ce30/79950cfa/65316d2bc051413294285f1e_mlopsworldthumbnail.png)
The MLOps movement and associated new tooling is starting to help tackle the very real technical debt problems associated with machine learning in production.
