Feature pipelines in idiomatic Python
Built-in scheduling, streaming + caching
Composed & queried in real-time
Toolchain for LLMs
Machine learning infrastructure is painful
Chalk makes it simple for data teams to focus on building the unique products and models that make their businesses thrive.
Chalk has become a critical component of our Risk Intelligence Platform. It expanded Ramp's capabilities with online machine learning and enabled us to scale safely by powering our transaction fraud model and credit underwriting process.
Ryan Delgado Director of Engineering, Data Platform
Deploy to your own infrastructure.
Use your existing database as your online + offline store. No bespoke storage. Everything in your cloud.
High-volume workloads at ultra-low latency.
Chalk’s Compute engine scales horizontally out-of-the-box and executes most complex queries on a Rust-based runtime for maximum performance. 100,000 QPS in < 5ms? We have you covered.
Power real-time decisions with real-time data.
Make better predictions with fresher data. Don’t pay vendors to pre-fetch data you don’t use. Query data just-in-time for online predictions.
Perfect auditability
Know everything you computed and data replay anything.
Parallel Resolvers
This operator executes your Python code in Chalk's massively parallel low-latency runtime environment.
Execution time | 4ms |
Self time | <1ms |
Result size | 10MB |
Result | 500k rows |
Groups | 4 |
Groups size | 5MB |
Runtime | Rust |
Resolvers
Features
Output
Feature | Value | Value | |
---|---|---|---|
01 | pkey | 1 | 2 |
02 | recent_tx_amts | [130, 24, 87] | [999, 0, 0] |
03 | fraud_score | 0.26 | 0.13 |
04 | fraud_id | cle2k1 | clle09 |
05 | tx_distribution | norm | unif |
06 | authorization_code | 83823 | 19231 |
07 | authorized | true | true |
08 | name_match_score | 0.99 | 1.0 |
Unify training and serving. Iterate faster.
Experiment in Jupyter, then deploy to production.
Prevent train-serve skew and create new data workflows in milliseconds.
Detect, troubleshoot, and eliminate data issues
Track data use, drift, and quality effortlessly with observability—built right in.
Jupyter Notebook
dashboard.chalk.ai
Blog + News
The Chalkboard
Feature Store at Work
A Tutorial on Fraud and Risk
July 11, 2024
The Chalkboard
Introducing Chalk NY
July 1, 2024
CB Insights
Chalk AI named to CB Insights AI 100 list for 2024.
April 2, 2024
The Information
What Separates the AI Winners and Losers; The General Catalyst-Backed Startup Taking On Databricks
December 12, 2023
Get Started with Code Examples
Unlock the power of real-time pipelines.
Withdrawal Model
Fraud & Risk
Decide and enforce withdrawal limits with custom hold times.
Income
Credit
Compute income from Plaid transactions.
Cache Busting
Caching
Bypass the cache with a max-staleness of 0.
Device Data
Predictive Maintenance
Easily listen to streaming data and parse messages with custom logic.
Start building with Chalk
Contact Us