Chalk

About Chalk

Chalk is a real-time data platform that enables machine learning and generative AI by providing a feature store and compute engine optimized for high-volume workloads with ultra-low latency. It allows data teams to unify training and serving, facilitating rapid experimentation and deployment while ensuring data quality and observability.

```xml <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. </problem> <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. </solution> <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 </features> <target_audience> Chalk is designed for data scientists, machine learning engineers, and data platform teams building real-time ML and generative AI applications. </target_audience> ```

What does Chalk do?

Chalk is a real-time data platform that enables machine learning and generative AI by providing a feature store and compute engine optimized for high-volume workloads with ultra-low latency. It allows data teams to unify training and serving, facilitating rapid experimentation and deployment while ensuring data quality and observability.

Where is Chalk located?

Chalk is based in San Francisco, United States.

When was Chalk founded?

Chalk was founded in 2022.

How much funding has Chalk raised?

Chalk has raised 10000000.

Who founded Chalk?

Chalk was founded by Elliot Marx and Andrew Moreland.

  • Elliot Marx - Co-Founder
  • Andrew Moreland - Co-Founder
Location
San Francisco, United States
Founded
2022
Funding
10000000
Employees
35 employees
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Chalk

Score: 100/100
AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

Chalk is a real-time data platform that enables machine learning and generative AI by providing a feature store and compute engine optimized for high-volume workloads with ultra-low latency. It allows data teams to unify training and serving, facilitating rapid experimentation and deployment while ensuring data quality and observability.

chalk.ai2K+
cb
Crunchbase
Founded 2022San Francisco, United States

Funding

$

Estimated Funding

$10M+

Team (30+)

Elliot Marx

Co-Founder

Andrew Moreland

Co-Founder

Company Description

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.

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

Target Audience

Chalk is designed for data scientists, machine learning engineers, and data platform teams building real-time ML and generative AI applications.

Chalk - Funding: $10M+ | StartupSeeker