It’s like GitHub for ML.
Turn your Python functions into reusable and versioned datasets or models with only a single line of code.
Bring all your metadata to a single place. Store your datasets, register your models, and log your metrics. You can log your parameters, charts, metrics, and plots to Layer and can compare them between different versions of datasets and models, enabling experiment tracking and governance.
Act fast on unexpected data flow or quality changes and sudden model performance degrades with powerful assertions like assert_skewness() and assert_unique(), and by logging your parameters, charts, metrics, and plots to Layer.
from layer.decorators import dataset
@assert_skewness("Price", -0.3, 0.3)
columns = ["Id", "Product", "Price"]
df = pd.DataFrame(data, columns=columns)
Every team member is on the same page. Read and write from and to the same central ML repository for datasets, models and documentation.
Say no to inaccessible data, undiscoverable models and unknown metadata. Layer gives your team the ability to locate projects, find entity versions, and discover data; enhancing collaboration and reducing the time barrier on new projects.
Ease new hire onboarding with a central repo for projects and high-quality READMEs, with easy discoverability on models and data, so that team members can know and understand other projects than their own.
Layer solves the headache of inaccessible data, unreproducible experiments and scattered documentation that results in harsh project handovers. In Layer, each project has a dedicated spaces where all documentation and ML entities are found in one place allowing easy presentation and communication.
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→
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→