In the latest quarter, Chalk introduced several platform enhancements to bolster its ability to support complex, production-grade machine learning (ML) systems. These improvements focus on enabling teams to define complex features, observe and debug runtime behavior, control infrastructure at scale, and integrate Chalk into production ML workflows. Key updates include the introduction of native support for prompt evaluations and multimodal inputs in large language model (LLM) workflows, enhanced feature flexibility through the addition of over 50 new Velox expressions, and improved SQL-native workflow support with ClickHouse integration. The updates also include enhanced observability with workspace-level audit logs and Parquet exports, infrastructure control through per-pod rate limiting and node pool isolation, and the release of a new TypeScript gRPC SDK. Chalk's practical applications are demonstrated in industries like fraud detection, credit underwriting, and identity risk scoring, with new educational resources available to help teams integrate Chalk into their ML stacks effectively.