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AI-based Self-Service Feature Platform

Embark on an effortless journey in Machine Learning with FeatureByte, where innovation meets simplicity. Our platform, designed for both novices and experts, revolutionizes the way you create, manage, and serve Machine Learning features. Whether you're a startup or a large enterprise, FeatureByte is tailored to scale with your AI ambitions.

You can benefit from FeatureByte in two ways:

Take charge of the entire ML feature lifecycle

Feature Engineering and management doesn’t have to be complicated. Take charge of the entire ML feature lifecycle. With FeatureByte, you can create, experiment, serve and manage your features under one roof.

Create with Ease:
  • Create and share state-of-the-art ML features effortlessly - let FeatureByte handle the complexity of time-aware SQL.
  • Reuse and tailor features for your specific use cases.
  • Bring your UDF to leverage the power of transformer models within FeatureByte.
Create and Save Feature
# Get view from catalog
invoice_view = catalog.get_view("GROCERYINVOICE")
# Declare features of total spent by customer
# in the past 7 and 28 days
customer_purchases = invoice_view.groupby(
    "GroceryCustomerGuid"
).aggregate_over(
    "Amount",
    method="sum",
    feature_names=[
        "CustomerTotalSpent_7d",
        "CustomerTotalSpent_28d"
    ],
    fill_value=0,
    windows=['7d', '28d']
)
customer_purchases.save()
Experiment Featurelist
# Get feature list from the catalog
feature_list = catalog.get_feature_list(
    "200 Features on Active Customers"
)
# Get an observation set from the catalog
observation_set = catalog.get_observation_table(
    "5M rows of active Customers in 2021-2022"
)
# Compute training data and
# store it in the feature store for reuse and audit
training = \
    feature_list.compute_historical_feature_table(
      observation_set,
      name="Training set to predict purchases next 2w"
    )
Experiment Boldly:
  • Instant access to historical features.
  • Innovate faster with live data experimentation at scale.
Serve Swiftly:
  • Deploy AI data pipelines and serve features with minimal latency.
  • Maintain data consistency between training and inferencing phases.
Deploy and Serve Feature List
# Get feature list from the catalog
feature_list = catalog.get_feature_list(
    "200 Features on Active Customers"
)
# Create deployment
deployment = feature_list.deploy(
    name="Features for customer purchases next 2w",
)
# Activate deployment
deployment.enable()
# Get shell script template for online serving
deployment.get_online_serving_code(language="sh")
Define Data Cleaning Policy on Table
# Get table from catalog
items_table = catalog.get_table("INVOICEITEMS")

# Discount must not be negative
items_table.Discount.update_critical_data_info(
    cleaning_operations=[
        fb.MissingValueImputation(
            imputed_value=0
        ),
        fb.ValueBeyondEndpointImputation(
            type="less_than",
            end_point=0,
            imputed_value=0
        ),
    ]
)
Manage Effectively:
  • Centralize and streamline your feature engineering processes.
  • Monitor and maintain the health of your feature pipelines.

Scale Your Enterprise AI Efforts with FeatureByte

Looking to amplify your AI operations at an enterprise scale?

Discover the extensive capabilities of FeatureByte Enterprise:

  • AI-Powered Copilot: Automatically generate state-of-the-art features tailored to your use case, enhancing efficiency and innovation.
  • User-Friendly Interface: Facilitate effortless collaboration and efficient management.
  • Self-Organizing Feature Catalog: Promote feature reuse and reduce redundancy, driving productivity and creative solutions.
  • Robust Governance: Enforce role-based permissions and streamline the feature life cycle with our advanced versioning and approval workflow system.

Contact us today for a demonstration or to learn more about how we can help you transform your AI aspirations into reality.