DataChef

Foundation models dominate the AI conversation, but most real enterprise value still sits in structured data — churn, fraud, pricing, demand, risk — where AutoML quietly outperforms LLMs on accuracy, cost, and reliability. This 45-minute masterclass, based on the O'Reilly book *Learning AutoML*, gives technical practitioners a clear framework for when to reach for AutoML, when to reach for foundation models, and when the right answer is to combine them.

The session opens with a decision framework: where AutoML wins on tabular and time-series problems, where LLMs add leverage (feature engineering, semi-structured data, explanations), and the failure modes of each. We then run a live, end-to-end walkthrough on a real dataset — data prep, model search, leaderboard interpretation, calibration, and deployment — using AutoGluon as the working example. The final segment covers what it takes to industrialize AutoML in production: governance, monitoring, drift, retraining cadence, and the organizational patterns that separate pilots from durable systems.

Attendees will leave with a working mental model for AutoML vs. foundation models, a reusable code template, and a checklist for taking AutoML from notebook to production.

Format: Lecture with live demo. Technical literacy assumed (Python, basic ML). Duration: 45 minutes.

** Please note: Space is limited to 19 seats and is available on a first-come, first-served basis. We recommend arriving early to secure your spot. **

Most modernization timelines aren't set by the complexity of the legacy system. They're set by how much of the organization's institutional knowledge lives in the heads of a few people, and how many meetings it takes to move that knowledge somewhere it can be acted on. The more that knowledge stays locked to individuals, the more your organization compensates with committees, sign-offs, and "let's get everyone aligned" sessions, and the slower everything moves, regardless of the tech stack. What's changed is that this is no longer a purely human bottleneck. AI now makes it possible to capture and scale that knowledge directly, instead of routing it through another meeting. This roundtable is a closed-door conversation for leaders who've lived this: where does your organization's real modernization bottleneck actually sit, and what would it take to decouple critical knowledge from the people who currently gatekeep it?