Now runs inside your
DAG

The collaboration-first ML metadata store

Log, display, compare and share your Datasets, Models and Project Documentation in one single place.

Dataset and Model Versioning

Track and reproduce experiments, effortlessly.

Record your learnings, datasets and models along the project journey with layer.log(), @dataset and @model decorators. Layer automatically versions your entities so that you can precisely repeat experiments and track their history.

Learn more

from layer.decorators import model

@model("california_housing")
def train():
 model = XGBClassifier()
 model.fit(X_train, y_train)
 layer.log({...})
return model

train()

Metadata Store

All in one place — never work in a silo again.

Close the collaboration gap and ease new hire onboarding with central repos for your datasets, ML models, metadata, and documentation. Log parameters, charts, metrics and more; quickly locate ML entities, create project documentation, and handover seamlessly.

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Assertions and Monitoring

Worried about reliable pipelines?Don’t be.

Unreliable production pipelines suck. Layer automatically versions datasets and models as the sanity check on pipelines running in production.

Layer also helps you to track performance shifts such as model performance drift or data quality issues with simple assertions like assert_not_null() and assert_true().

Learn more

from layer.decorators import dataset

@dataset("product-data")
@assert_skewness("Price", -0.3, 0.3)
def create_my_dataset():

data = [[1, "p1", 15], [2, "p2", 20], [3, "p3", 10]]
columns = ["Id", "Product", "Price"]
df = pd.DataFrame(data, columns=columns)
return df
Fabrics

More computing power? You got it.

Lack of computing power limits complex projects. Layer’s cloud resources allow you to train your models on powerful pre-defined GPU and CPUs. Layer handles all the pain and complexity of infrastructure. If you don’t need the power, use your own resources.

Learn more

import layer

@layer.model("california_housing")
@layer.fabric("cpu-cluster")
def
train():
 model = XGBClassifier()
 model.fit(X_train, y_train)
 layer.log({...})
return model

layer.run([train])

DBT Adapter

Use DBT? Integrate ML pipelines into dbt’s DAG within SQL.

Layer natively integrates with DBT so that you can use ML pipelines, make them a part of the DAG, and scheduled jobs all within DBT (using SQL).

Learn more

SELECT
 id,
 layer.predict(
'layer/models/review_score_predictor',
 review)

FROM
 {{ ref ('products') }]

Community

The definitive ML project development diary

Show off your work with demo-able & easy-to-present dynamic content for your ML projects.

See community examples
Corporate

Built with data teams, for data teams

Layer is the single place for all datasets, models and documents developed by your team.

Request a demo