Feature Stores & Their Role in Mlops
Feature Stores Are a Part of the Machine Learning Workflow and Ml Tooling. Feature Stores Fit Themselves Between the Data and the Machine Learning Models. What...
Feature stores are a part of the machine learning workflow and ML tooling. Feature stores fit themselves between the data and the machine learning models.
What is a feature store?
Feature stores are a way to manage data, particularly for machine learning operations (MLOps).
A simple explanation of MLOps is all the engineering pieces you have to bring together in order to deploy, run, and train AI models. As such, MLOps weaves together significant components of:
- Machine learning
- DevOps
- Data engineering
With feature stores, the craftsmanship of ML teams can increase because their data—the lifeblood of any ML model—is more easily seen.
(Read our machine learning vs AI comparison.)
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How feature stores work
Data used to sit comfortably in a server somewhere. Data scientists would access the data when it was needed for analysis. Or, users would query the server to display data.
But, in the era of big data, this type of data storage and recall is no longer feasible.
Machine learning, and the construction of AI systems, presents a different kind of use case: Different data needs to be pulled from their sources, and then transformed and combined to create a standard feature set to serve a model.
Teams face challenges when putting ML models into production. Mike Del Basco, the co-founder of Tecton.ai and a creator of Uber’s Michelangelo ML platform, says teams face these challenges:
- Accessing the right raw data
- Building features from raw data
- Combining features into training data
- Calculating and serving features in production
- Monitoring features in production
Feature stores solve a problem around the increasing complexity of data demands made by the ML workflow. Data scientists want—need—to be able to easily:
- Verify the validity of the data
- Check its quality
- Know it’s fresh
- Version the data and share it with others
Feature stores are positioned between the ML models and the data sources to accommodate the kind of accessibility that Machine Learning teams require.
(Source)
Feature stores help teams:
- Better collaborate
- Reduce duplication
- Better regulatory compliance
- Faster development
Where data is the fundamental component of an ML model, feature stores increase a team’s ability to:
- Communicate about their data
- Create more unique ML models.
David Miller brings 15 years of experience in global economics, personal finance strategy, and market dynamics. He specializes in turning complex economic trends into actionable insights for everyday readers.
