Prep & Deploy AI Data in Minutes with FeatureByte + Databricks

We’re excited to announce that FeatureByte now seamlessly integrates with Databricks, introducing a powerful solution for organizations seeking to accelerate their ML lifecycle. FeatureByte Copilot is a force multiplier for data scientists, allowing them to prepare and deploy AI data pipelines in minutes, instead of weeks or months. This powerful capability is now tightly integrated with Databricks’ robust data intelligence platform, delivering massive value to organizations.

Why Databricks + FeatureByte?

FeatureByte Copilot is an innovative platform that leverages Gen AI and IP created by some of the best data scientists in the world, to automate data preparation and deployment. Data prep and pipeline deployment for AI/ML have been a challenge for practitioners since the early days of Data Science. FeatureByte empowers data scientists and ML engineers to go from raw data to production-ready feature pipelines in minutes instead of weeks or months or 50X faster, at 1/5th the cost. 

Databricks has spent a decade building a platform that helps enterprises bring AI and analytics to their data, and deploy intelligent solutions at scale. The Databricks feature store is a centralized repository that enables data science teams to find and share ML features and also ensures that the same code used to compute the feature values is used for model training and inference. It is a critical part of the ML infrastructure to scale AI across the organization. 

Even with a powerful feature store, data scientists and ML engineers need to do a massive amount of painstaking work to ideate, create and deploy features that make up exceptional ML models. That’s where FeatureByte comes in: acting as a data science copilot, we radically simplify the entire feature lifecycle and let data science teams do more with fewer resources. 

The integration brings the power of FeatureByte to Databricks users and customers, while allowing them to maintain complete ownership of their data. FeatureByte leverages the power of the Databricks platform to push all computations where the data already lives – in the Databricks platform. Not only that, all features created and computed by FeatureByte are available in the Databricks feature store for serving and are registered in the Unity Catalog for simplified management. 

With the addition of FeatureByte on Databricks, organizations can unlock millions of dollars of hidden value by speeding up the Machine Learning lifecycle: 

Databricks + FeatureByte in Action

The quality and richness of its features are the core of every successful machine learning model. Let’s explore how FeatureByte and Databricks work together to elevate the value of ML initiatives. 

Data Exploration and Feature Engineering

Users start by registering Databricks tables and  feature store within FeatureByte. FeatureByte automatically detects the data semantics for each column, and ideates features specific to a use case based on those semantics and relationships between tables. This process alone can be months of painstaking effort for a data scientist, which FeatureByte completes in minutes.

FeatureByte’s seamless UI makes it easy to explore feature ideas and manage features in an intelligent catalog. Data engineers and data scientists can explore vast datasets and experiment with feature engineering techniques without writing a single line of code if they choose to. Administrators can restrict access  for each FeatureByte catalog to a DataBricks user group, ensuring that Databricks’ RBACs remain consistent within FeatureByte.

Model Training and Experimentation

Once the features are built, the focus shifts to training and fine-tuning machine learning models. Point-in-time correct historical data can be easily generated via either the FeatureByte SDK or UI. FeatureByte’s integration with Databricks offers a scalable and collaborative platform for model experimentation. Once the training dataset is available, users can seamlessly train models directly from a Databricks Notebook. 

FeatureByte automatically tracks and saves training data so data scientists can reuse it in the future. Users can view details of historical training datasets, including who created the dataset, what features are involved, which observation tables were used, and what the data looks like.

With FeatureByte on Databricks, model training becomes a fast, iterative and collaborative process, resulting in higher-performing models.

Feature and Model Deployment

With FeatureByte on Databricks, the journey from model development to deployment is seamless. Users can deploy feature pipelines directly from FeatureByte. Once deployed, FeatureByte publishes offline feature tables to the Databricks feature store and ensures they are updated for freshness. Users can then save and log the model in MLFlow and register it in Unity Catalog. This streamlined deployment process ensures that models are deployed swiftly and efficiently, ready to serve predictions at scale.

For organizations with diverse use cases, FeatureByte on Databricks offers unparalleled flexibility in serving predictions. Whether through batch or low-latency serving, teams can seamlessly adapt scoring to meet their specific requirements.

Accelerate Your Feature Pipelines with FeatureByte + Databricks

The integration of FeatureByte with Databricks empowers organizations to accelerate their machine learning pipelines and drive actionable insights at scale — all while leveraging a platform they’re comfortable using. With FeatureByte on Databricks, the journey from data to predictions becomes not just efficient but transformative, accelerating innovation for enterprise AI.

If your organization is currently using Databricks or exploring a Databricks feature store implementation, let’s discuss how FeatureByte could be a critical addition to your AI stack.