Ship Better Models: A New ML Workflow with a Context Layer and Agents

Live Webinar

Thursday, August 13
10:00 am PT/1:00 pm ET

Model development stalls on long iteration cycles, too many tools, and constant handoffs. We learned this firsthand with enterprise teams building real-time models, from fraud detection for card authorization to recommendation systems for e-commerce.

Chalk co-founder Elliot Marx tells the story of building an AI agent that helps data teams investigate missed fraud, analyze model behavior, and propose new rules and features. The agent needed the same context as the model, and the batch-first infrastructure underneath could not support that. So we rebuilt the foundation around a real-time context layer that computes fresh data from the source at inference time.

What you'll learn:

• Where batch-first stacks break: stale features, train/serve skew, inconsistent feature definitions, and latency limits under real-time demand.
• How a shared context layer speeds up models: when the agent and model read from the same fresh context, investigation and iteration get faster.
• The engineering decisions that mattered: deployment constraints, the freshness-versus-speed tradeoff, and observability as a requirement.
• How agents create a continuous cycle: investigate missed fraud, analyze behavior, propose new rules and features.

See where batch-first stacks fall short and what a real-time context layer changes for your models and agents. Save your seat and register today.