Chalk provides a unified data platform designed specifically for building and serving AI and machine learning applications. It offers ultra-fast data pipelines, on-demand compute, and built-in scheduling and caching within the customer's cloud environment. This infrastructure simplifies data engineering workflows, enabling teams to deploy real-time models with low latency and maintain auditability across training and serving.
Funding
$10M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Data teams face challenges in unifying data for machine learning (ML) and generative AI workflows, leading to slow experimentation and deployment cycles. Existing infrastructure often struggles to deliver the low-latency performance required for real-time decision-making. Maintaining data quality and observability across training and serving environments adds further complexity.
Solution
Chalk provides a real-time data platform designed to streamline ML and generative AI development. It offers a feature store and compute engine optimized for high-volume workloads with ultra-low latency. The platform enables data teams to define feature pipelines in Python and query them in real-time, unifying training and serving data. Chalk's architecture supports built-in scheduling, streaming, and caching, allowing for rapid experimentation and deployment. It integrates with existing data infrastructure and provides tools for monitoring data quality, detecting drift, and troubleshooting issues.
Target Audience
Chalk is designed for data scientists, machine learning engineers, and data platform teams building real-time ML and generative AI applications.
Features
- Feature pipelines defined in idiomatic Python, powered by a Rust-based runtime for performance
- Built-in scheduling, streaming, and caching capabilities
- Compute engine that scales horizontally for high-volume workloads at ultra-low latency (100,000 QPS in under 5ms)
- Integration with existing databases (PostgreSQL, Snowflake, etc.) as online and offline stores
- Parallel resolvers for executing Python code in a massively parallel, low-latency environment
- Observability tools for tracking data use, drift, and quality
- Integrations with tools like Datadog, PagerDuty, and Slack
- Support for feature versioning and backfilling data