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.

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 Real-time-serving 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

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

In []

df = client.offline_query( inputs={User.id: user_ids}, outputs=[ User.name, User.credit_report, User.account.bal, ] )

In []

# xgboost train / predict xgb = XGBClassifier( eval_metric="logloss", use_label_encoder=False )

dashboard.chalk.ai

How we partner with your team

Choose the engagement that fits your workload and your needs.

On-demand expertise

Get technical guidance when you need it.

Chalk’s forward-deployed engineers work with your AI, data, and business teams to define the workload, evaluate the tradeoffs, and map the path to production. Get help with hard decisions as you build, like what it takes to meet your latency, reliability, and security requirements.

Managed sprints

Let Chalk take a workload to production.

Bring us a high-priority AI/ML workload and we’ll help take it from design through deployment. We can fine-tune, evaluate, and deploy models using your data and infrastructure. A turnkey path from idea to custom AI deployment.

Side-by-side capacity

Extend your team with Chalk engineers.

Chalk engineers work alongside your engineers in your repo and cloud. They pair on implementation, open pull requests, and help build and operate production AI systems. Your team gets additional engineering capacity.