Chalk | Real-time platform for machine learning

Feature pipelines in idiomatic Python

Powerful data engineering workflows, without the infrastructure headaches. Powered by Rust.

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.

user

chalk

query

get_plaid

Apple Watch Series 3

$299.00

Processing...

from chalk.api import ChalkClient ChalkClient().query({ inputs={ Transfer.user.id: 182831, Transfer.amount: 299.00 }, outputs=[Transfer.user.score], })

get_credit_report

name_match_score

is_income_txn

get_user

get_plaid

get_failed_logins

login_counter

200 OK { name: "J.J. Chusterton", age: 28, accounts: [ "Bank of America", "Chase", "First Republic" ] }

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

Group 1

Group 2

Group 3

Group 4

	Feature	Value	Value	Value	Value
01	pkey	1	2	3	4
02	recent_tx_amts	[130, 24, 87]	[999, 0, 0]	[8, 3]	[4.38]
03	fraud_score	0.26	0.13	0.89	0.02
04	fraud_id	cle2k1	clle09	cln3ud	cls93s
05	tx_distribution	norm	unif	null	unif
06	authorization_code	83823	19231	null	38324
07	authorized	true	true	false	true
08	name_match_score	0.99	1.0	0.12	0.98