By Ben Rogojan, Data Science and Data Engineering Solutions Architect
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Whenever I look to learn a new topic, I create some form of learning plan. There is so much content out there that it can be difficult to approach learning in the modern era.
It’s almost comical. We have so much access to knowledge that many of us struggle to learn because we don’t know where to go.
This is why I put together roadmaps and learning plans.
Below is my MLOps learning plan that I will be taking on for the next few months.
The focus will be on first taking a quick refresher in ML as well as taking an advanced Kubernetes course.
From there I will be focused on Kubeflow, Azure ML, and DataRobot.
Background Of MLOps
In 2014 a group of Google researchers put out a paper titled Machine Learning: The High-Interest Credit Card of Technical Debt. This paper pointed out a growing problem that many companies might have been ignoring.
Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. [D. Sculley, Gary Holt, etc]
Another way the researchers put this in a follow-up presentation was that launching a rocket was easy, but ongoing operations afterwards was hard. Back then, the concept of DevOps was still coming into its own, but these engineers and researchers realized that there were many more complications in terms of deploying a machine learning model vs. deploying code.
This is when the popularity of machine learning platforms began to rise. Eventually, many of these platforms adopted the term MLOps to explain the service they were providing.
That begs the question. What is MLOps?
What Is MLOps?
Machine Learning Operations, or MLOps, helps simplify the management, logistics, and deployment of machine learning models between operations teams and machine learning researchers.
From a naive perspective it is just DevOps applied to the field of machine learning.
But, MLOps actually needs to manage a lot more than what DevOps usually manages.
Like DevOps, MLOps manages automated deployment, configuration, monitoring, resource management and testing and debugging.
A possible machine learning pipeline could look like the image below.
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Unlike DevOps, MLOps also might need to consider data verification, model analysis and re-verification, metadata management, feature…
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