machine learning systems

4 statements across 4 episodes · 0 bullish · 0 bearish · 4 people on the record · first statement Nov 23, 2015 by Josh Bloom · across every show →

Everything said about machine learning systems, oldest first

Nov 23, 2015

Insight

Bloom: Feedback loops transition ML from human augmentation to automation

“Over time, if you build the appropriate feedback loops into your systems, the system itself will wind up learning from those processes and get better and better, so you can actually start automating those processes.”

Josh Bloom Nov 23, 2015 ▶ 14:35 Machine Learning in Production with Josh Bloom, Co-founder Wise.io

Jan 25, 2016 neutral

Prediction Not checkable as stated

Scholnick: Humans will remain heavily involved in ML for 5-10 years

“So, at least for you know, I think the next five to 10 years, there's going to be heavy involvement of humans in machine learning systems.”

Dan Scholnick Jan 25, 2016 ▶ 12:11 Investing in Data and A.I. // Dan Scholnick, Trinity Ventures (Hosted by FirstMark Capital)

Feb 25, 2019

Insight

Machine learning systems must trigger pipelines based on data or model drift

“Code drift there, which arguably is the number one, is the only trigger for most web apps. You know, your code drift, someone adds something, you rebuild your model, which is your web application. But actually what you care about in these new systems is data d…”

Dillon Erb Feb 25, 2019 ▶ 9:56 CI/CD for Machine Learning & AI // Dillon Erb, Paperspace (FirstMark's Data Driven NYC)

May 16, 2024

Insight

Machine learning's economic value lies in getting from 80% to 98% accuracy

“Machine learning systems it's easy to get to an 80% solution, but all the values in that marginal 15 or 18 to get to 95 to 98% accuracy.”

Tomasz Tunguz May 16, 2024 ▶ 25:57 AI, Data and Blockchain: a VC perspective | Tomasz Tunguz, Founder of Theory Ventures

machine learning systems

Everything said about machine learning systems, oldest first

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