How to Find the Agent Failures Your Evals Miss with Scott Clark - #767
In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Maslow’s hierarchy of observability: telemetry for logging, monitoring for known signals, and post-production or online analytics to surface unknown unknowns. We dig into examples of real-world failures Scott’s team has seen in production systems, such as “lazy” tool-use hallucinations that standard evals miss, and how mapping traces into vector fingerprints enables clustering and topic discovery to uncover emergent behaviors. Scott explains how analytics can feed the data flywheel by generating evals, guardrails, and training data, and why online, adaptive approaches are essential for non-stationary models. We also touch on practical how-to’s such as instrumentation with OpenTelemetry, the GenAI semantic conventions, and the role of dedicated analytics tools.
The complete show notes for this episode can be found at https://twimlai.com/go/767.
Automated Model Tuning with SigOpt - #324
In this TWIML Democast, we're joined by SigOpt Co-Founder and CEO Scott Clark. Scott details the SigOpt platform, and gives us a live demo!
This episode is best consumed by watching the corresponding video demo, which you can find at twimlai.com/talk/324.
Supporting Rapid Model Development at Two Sigma with Matt Adereth & Scott Clark - TWIML Talk #273
Today we’re joined by Matt Adereth, managing director of investments at Two Sigma, and return guest Scott Clark, co-founder and CEO of SigOpt, to discuss:
• The end to end modeling platform at Two Sigma, who it serves, and challenges faced in production and modeling.
• How Two Sigma has attacked the experimentation challenge with their platform.
• What motivates companies that aren’t already heavily invested in platforms, optimization or automation, to do so, and much more!
a16z Podcast: On Data and Data Scientists in the Age of AI
Data, data, everywhere, nor any drop to drink. Or so would say Coleridge, if he were a big company CEO trying to use A.I. today -- because even when you have a ton of data, there's not always enough signal to get anything meaningful from AI.
Why? Because, "like they say, it's 'garbage in, garbage out' -- what matters is what you have in between," reminds Databricks co-founder (and director of the RISElab at U.C. Berkeley) Ion Stoica. And even then it's still not just about data operations, emphasizes SigOpt co-founder Scott Clark; your data scientists need to really understand "What's actually right for my business and what am I actually aiming for?" And then get there as efficiently as possible.
But beyond defining their goals, how do companies get over the "cold start" problem when it comes to doing more with AI in practice, asks a16z operating partner Frank Chen (who also released a microsite on getting started with AI earlier this year)? The guests on this short "a16z Bytes" episode of the a16z Podcast -- based on a conversation that took place at our recent annual Summit event -- share practical advice about this and more.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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Bayesian Optimization for Hyperparameter Tuning with Scott Clark - TWiML Talk #50
As you all know, a few weeks ago, I spent some time in SF at the Artificial Intelligence Conference. While I was there, I had just enough time to sneak away and catch up with Scott Clark, Co-Founder and CEO of Sigopt, a company whose software is focused on automatically tuning your model’s parameters through Bayesian optimization. We dive pretty deeply into that process through the course of this discussion, while hitting on topics like Exploration vs Exploitation, Bayesian Regression, Heterogeneous Configuration Models and Covariance Kernels. I had a great time and learned a ton, but be forewarned, this is most definitely a Nerd Alert show! Notes for this show can be found at twimlai.com/talk/50
#51: SigOpt: Optimizing Everything with Python
See the full show notes for this episode on the website at talkpython.fm/51
