Welcome to Data Management Newsletter #5, where I curate interesting articles on data management and data protection for data practitioners and executives. For this week's newsletter, I want to share a long-form article I wrote about DevOps, MLOps, and AIOps. It's an overview of the MLOps and AIOps worlds, to help understand what they mean, how they relate to DevOps, and how they compare in terms of benefits.
Why did I choose to write about MLOps and AIOps?
MLOps and AIOps are two similar-sounding terms that are used to refer to vastly different disciplines within the industry today. Ever since the introduction of these terms a few years ago, zeitgeist interest in them has surged, as this Google Trends chart shows.
And yet, except for a handful of practitioners that are actively working on projects in these areas, for most casual readers, or even enthusiasts looking to explore the space, the meaning of MLOps and AIOps, and their benefits, come across as ambiguous, overlapping, and undifferentiated (relative to each other).
So, I wanted to clarify what MLOps and AIOps mean, what problems they are meant to solve, and what tools exist for teams that are looking to adopt them into their product and service building strategies.
You can read the complete post here!
This OpenSource article attempts to do the same. The article is short but does a good job of hitting all the right points IMO!
I've referenced O'Reilly's "AI Adoption in the Enterprise 2021" report in my post but I'm posting it here again for convenience as it contains a number of very interesting insights about AI / ML technology's maturity and adoption in enterprises today.
That's all for this week's edition of the Data Management Newsletter.
Cheers, and hope you're having a great weekend!