HeyJaredFuture Proof Data Science
A weekly newsletter for data scientists who want to stay relevant and grow their careers in the age of AI (and beyond)
- Indexed issues, last 90 days
- 7
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- Sep 15, 2026
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- Jul 7, 2026
Latest issues
My Experience Building Agentic Analytics at a Tech Company (And Shipping it Stakeholders) (opens the original)
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There’s a lot of talk about agentic analytics right now.“Talk to your data” solutions, AI-powered BI, semantic layers, self-service analytics 2.0, etc.It seems like every analytics enterprise tool out there is pushing hard to integrate AI into every corner of their solution.And well, it’s no surprise that everyone on social media has an opinion on where this is heading. But very few are showing what it looks like to build this inside a real company, let alone answer the question: did it actually
Shipping Models Is Not the Same as Shipping Systems (opens the original)
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You’ve probably seen this statistic floating around: “87% of ML models never make it to production.”The number has been quoted a lot since 2019. The problem is that it's just not fully accurate anymore, and hasn't been for a few years.But it would be a mistake to simply dismiss it, because the underlying point still holds, and every conversation about the value data scientists should bring to companies eventually circles back to it.It all comes down to this idea: shipping models is not the same
Docker For Data Scientists (Simplified) (opens the original)
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ML System Design for Data Scientists (How Senior Data Scientists Actually Think About ML Systems) (opens the original)
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Something I've noticed over the years working with data scientists at all levels...A lot of times, seniors and juniors don’t differ too much in raw skill. Both can build models, both can tune them, both can read a paper and implement it. And thanks to AI, that technical gap is becoming even narrower.But when you ask a senior data scientist to build an ML model, they don’t start with the model itself (like most juniors do); they start sketching out the whole system: where the data comes from, how
Google's TabFM: What Data Scientists Should Know About This New Foundation Model (opens the original)
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Think about the last few ML models you trained…They probably followed the same pattern: You had a dataset, you cleaned it, you built features, you picked an algorithm, you tuned it, you trained it on your data, and only then could you start getting predictions.And when a new dataset showed up, you did all of it again from scratch.That’s been the reality of ML for as long as most of us have been working in this field.On June 30, 2026, Google Research released something that is changing this compl
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