So far in our MLOps journey, we have created ML research and ML model-building pipelines as well as saved them in serialized form. Saving models this way allows us to now take that serialized ML model and load it into an application.

We will now take the saved ML model…

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In our first article, we introduced the basics of MLOps, now we will talk about our core application in our tech stack, Airflow. Airflow will be the central orchestrator for all batch-related tasks.

Swapping technologies

This tech stack is designed for flexibility and scalability. There should be no issues using alternative tooling…

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MLOps (Machine Learning Operations) is the practice of combining the lessons learned from DevOps for the productionisation of machine learning. Its role is to fill the gap between the data scientist and the machine learning consumers.

Machine Learning? Data Science?

Machine Learning can be understood as the process of applying a set of techniques…

When working on multiple Python projects it's common to run into issues with Python versioning, and package management. I am going to introduce two projects to help you tackle these common issues. I’m not going to take about the Conda project, simply because in my experience 90% of the time…


Data Engineering is a relatively new concept, although the skills have been around for some time. If you Google around you will find that the skills, tools, and job responsibilities will vary significantly. My approach is a broad, modern approach to the data engineering role. Many hyperspecialized roles also exist…

Brian Lipp

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