Why is ML in the real world difficult? This newsletter brings together the best articles, news and papers. By @rish_bhargava and @nihit_desai.mlopsroundup.substack.comJoined September 2020
I'll be speaking at the #DataAISummit in San Francisco this year! On drift, model monitoring and feedback for human-in-the-loop #MachineLearning systems
Check out the complete agenda (with a stellar lineup of keynotes and breakout sessions) here: databricks.com/dataaisummit/n…
[New Post] The 2022 edition of Emerging Architectures for Data Infrastructure is out today!
We argue *platforms* are taking shape in the data ecosystem (though very early) - detailed architectures & analysis in the post
(w/ @martin_casado, @JenniferHli)
future.a16z.com/emerging-archi…
A new (mini) edition of our newsletter is out, with an exciting announcement:
"MLOps - from Models to Production", a 4-week course that we are developing in partnership with @corise_, is almost ready to go live! The first iteration starts 5/2. Details👇
mlopsroundup.substack.com/p/issue-31-a-n…
Happy Monday folks! Kick off the week with our latest newsletter. We cover @mattturck's ML Data and AI landscape; @eugeneyan's ApplyingML project; a paper on COVID border testing; @aronMaurer's post on blocking spam at Slack, and more.. Check it out:
mlopsroundup.substack.com/p/issue-28-mad…
I recently did an interview with @eugeneyan, where I share some thoughts on the machine learning infra and tooling ecosystem, and my experience building ML systems. You can check it out here: applyingml.com/mentors/nihit-…
In our latest edition, we cover a paper on the machine learning challenges in medical imaging, discuss a recent article on machine unlearning by @tsimonite, share @DoorDashEng strategies for matching supply and demand, and much more. Check it out:
mlopsroundup.substack.com/p/issue-27
Despite the potential of machine learning, it still isn’t a mainstream business tool.
Better data-centric MLOps tooling can play a major role in making sure every machine learning project is successful. 👉 openview.vc/3k6DuG6
New research alert!
Our own @andrew_reed_r and @NishaMuktewar have been exploring the issue of detecting concept drift when the cost of obtaining labeled data to validate model performance is high.
Check out our latest report here: concept-drift.fastforwardlabs.com
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fan of fun, Russian (sort of)
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