A Paradigm Shift for Enterprise Data
Benefit from a fundamental shift in how enterprise organizations develop curated data and accumulate domain and data “know-hows” as reusable assets.
Start Feature Discovery from Day One
Getting Started is Hard
Feature discovery requires deep data and domain knowledge. Involving different experts and stakeholders and the complexity and size of enterprise data add to this, making getting started difficult.
Jump-Start Your Process
Feature Factory automatically suggests feature spaces by analyzing your enterprise data. Analyze relational, transactional, temporal, and geolocation data to kick-start feature discovery and engineering and identify signals from day one.
Data-Centric and Programmatic Approach To Explore More Ideas
Manual Process Limits Your Ideas
Feature Engineering has – traditionally – been a highly manual, artisanal process. Your team’s ideas are constrained by a lack of time and resources, constraining the discovery of new and interesting paths.
Programmatic, Data-Centric Feature Engineering
Feature Factory lets you define feature spaces and auto-generates 100X broader feature hypotheses using a data-driven approach that expands your reach and your team’s ability to experiment adding to your existing data and feature knowledge
Transform Your “Know-How” into Reusable Assets
Feature Engineering is too Disposable
Feature engineering goes beyond simple SQL queries. Complex data operations and transformations, ETL, data cleansing, and feature transformations take time and require multiple iterations. However, the ad-hoc nature of this process means that when features are identified for specific use cases, the transformation steps taken to get there are usually lost in a sea of unused Jupyter notebooks.
Reusable Assets for Feature Engineering
dotData Feature Factory introduces the concept of reusable feature engineering assets. Stop reinventing the wheel by leveraging a repository of all recorded steps associated with discovered features, allowing your data science team to expand on already available feature discovery assets to accelerate their workflow.
From Jumbled Notebooks to Production-Ready Feature Pipelines
Hard to Take Features from Lab to Production
Feature discovery is typically performed inside each data scientist’s Jupyter Notebook. Notebooks quickly become an overwhelming jumble of code and are poorly managed or organized without standardization. Transforming this mess into production code can be challenging at best.
From Experiments to Production-Ready Code, Quickly
Feature Factory makes it simple for data science teams to build transparent, readable, and maintainable feature pipelines that are scaleable and cover edge cases when processing new data. Accelerate the process of moving from experiments to production with dotData Feature Factory.
How SMBC Accelerated Their Feature Development Process 48X
When SMBC – one of the World’s largest banks – wanted to accelerate their AI/ML development process, they turned to dotData’s Feature Factory platform. Download the case study to see how they accelerated development times by 4,800%.