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The Data Platform
for Machine Learning
Tired of Spark? So are we.
Just-in-time data
 + Hot-reload
 + Rust compute
Read the DocsRequest Demo

Feature pipelines in idiomatic Python. Powered by Rust.
With Chalk, feature pipelines are simple Python functions. Declare data
dependencies with Python type signatures, and Chalk will compose and execute
your pipelines to compute features in real time.
READ THE DOCS
Scheduling, streaming, caching – all built-in
Powerful data engineering workflows, without the infrastructure headaches.
READ THE DOCS
Automatically composed & queried in real-time
Chalk handles the composition of your pipelines to compute the features your
models need in real-time.
READ THE DOCS

1@features
2class Txn:
3  memo: str
4  payroll: bool
5  amount: float
6
7@online
8def is_payroll(memo: Txn.memo) -> Txn.payroll:
9   return "adp" in memo.lower()
10
11@online
12def income(
13  txns: User.txns[Txn.payroll is True]
14) -> User.income:
15   return txns


neobank
resolvers.py
100%
$

Feature pipelines in idiomatic Python. Powered by Rust.
With Chalk, feature pipelines are simple Python functions. Declare data
dependencies with Python type signatures, and Chalk will compose and execute
your pipelines to compute features in real time.
READ THE DOCS


neobank
resolvers.py
100%



neobank
resolvers.py
100%

Scheduling, streaming, caching – all built-in
Powerful data engineering workflows, without the infrastructure headaches.
READ THE DOCS
Automatically composed & queried in real-time
Chalk handles the composition of your pipelines to compute the features your
models need in real-time.
READ THE DOCS


neobank
resolvers.py
100%
$


Power real-time decisions with real-time data. Goodbye, ETL.
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.
Total. . . . . . . . $695.00
Buy Now Pay Later


python
typescript
cli

from chalk.api import ChalkClient

client = ChalkClient()
client.query(
  output=[User.income.last_60],
  deployment="jessie-2",
  input={
    Transfer.user.id: "dkjio4n902",
    Transfer.amount: 1200
  }
)




Served Credit Score
max
812
avg
637
Detect, troubleshoot, and eliminate data issues faster
Monitor feature values, drift, missing data, and pipeline performance. Logs,
metrics, and alerting – all built-in. Integrated with Slack, Pagerduty, and
Datadog.

Unify training and serving. Iterate faster.
Experiment in Jupyter, then deploy to production. Prevent train-serve skew and
speed up development.


neobank
resolvers.py
100%
$


Jupyter Notebook
In []:

df = client.offline_query(
  input=labels[[User.uid]],
  input_times=[datetime.now()] * len(labels),
  output=[
    User.name,
    User.credit_report,
    User.plaid_account.mean_balance,
  ]
)

Out[]:

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



Perfect Auditability
Unmatched data provenance. Know everything you computed and data replay
anything.
is_income
python
income_total
python
income_over_estimate
python
income_over_estimate
Aug 20, 2022 07:13:27+4:00
2ms

InputsUser.self_reported_income$24,000User.plaid_transaction_income$10,302OutputsUser.plaid_transaction_income$24,000Definition

get_user
v3
postgres://
get_plaid
v1
api.plaid.com (90ms)

Credit ApplicationUser
IDSPGRAY1980Date09/01/22Time09:15StatusDeclinedModelunderwriting_model

Integrations
Integrate with the tools you already use and deploy to your infrastructure

Get Started with Code Examples
Unlock the power of real-time data pipelines
SEE ALL EXAMPLES
Fraud & Risk
Withdrawal Model
Decide and enforce withdrawal limits with custom hold times.
View Code
Credit
Income
Compute income from Plaid transactions.
View Code
Caching
Cache Busting
Bypass the cache with a max-staleness of 0.
View Code
Predictive Maintenance
Device Data
Easily listen to streaming data and parse messages with custom logic.
View Code
Feature pipelines in idiomatic Python. Powered by Rust.
With Chalk, feature pipelines are simple Python functions. Declare data
dependencies with Python type signatures, and Chalk will compose and execute
your pipelines to compute features in real time.
READ THE DOCS


neobank
resolvers.py
100%

Scheduling, streaming, caching – all built-in
Powerful data engineering workflows, without the infrastructure headaches.
READ THE DOCS


neobank
resolvers.py
100%

Automatically composed & queried in real-time
Chalk handles the composition of your pipelines to compute the features your
models need in real-time.
READ THE DOCS


neobank
resolvers.py
100%
$


Power real-time decisions with real-time data. Goodbye, ETL.
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.
Total. . . . . . . . $695.00
Buy Now Pay Later


python
typescript
cli

from chalk.api import ChalkClient

client = ChalkClient()
client.query(
  output=[User.income.last_60],
  deployment="jessie-2",
  input={
    Transfer.user.id: "dkjio4n902",
    Transfer.amount: 1200
  }
)


Detect, troubleshoot, and eliminate data issues faster
Monitor feature values, drift, missing data, and pipeline performance. Logs,
metrics, and alerting – all built-in. Integrated with Slack, Pagerduty, and
Datadog.
Served Credit Score
max
812
avg
637
Unify training and serving. Iterate faster.
Experiment in Jupyter, then deploy to production. Prevent train-serve skew and
speed up development.
Jupyter Notebook
In []:

df = client.offline_query(
  input=labels[[User.uid]],
  input_times=[datetime.now()] * len(labels),
  output=[
    User.name,
    User.credit_report,
    User.plaid_account.mean_balance,
  ]
)

Out[]:

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


Perfect Auditability
Unmatched data provenance. Know everything you computed and data replay
anything.
is_income
python
income_total
python
income_over_estimate
python
income_over_estimate
Aug 20, 2022 07:13:27+4:00
2ms

InputsUser.self_reported_income$24,000User.plaid_transaction_income$10,302OutputsUser.plaid_transaction_income$24,000Definition

get_user
v3
postgres://
get_plaid
v1
api.plaid.com (90ms)

Credit ApplicationUser
IDSPGRAY1980Date09/01/22Time09:15StatusDeclinedModelunderwriting_model

Integrations
Integrate with the tools you already use and deploy to your infrastructure

Get Started with Code Examples
Unlock the power of real-time data pipelines
SEE ALL EXAMPLES
Fraud & Risk
Withdrawal Model
Decide and enforce withdrawal limits with custom hold times.
View Code
Credit
Income
Compute income from Plaid transactions.
View Code
Caching
Cache Busting
Bypass the cache with a max-staleness of 0.
View Code
Predictive Maintenance
Device Data
Easily listen to streaming data and parse messages with custom logic.
View Code



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