The Cold Start Problem for Machine Learning Problems

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A lot of the work of data scientists is involved in identifying data sources that they have access to and turning that data into features. A common Uber Eats example was trying to predict how long it's going to take until your till a order arrives at your doorstep. One pretty important feature would be the trailing number of orders a given restaurant has received over the last 30 minutes. And so if you now have a central catalog of trusted ML features that run in production, you can actually explore this catalog of existing features.

Summary
In order for a machine learning model to build connections and context across the data that is fed into it the raw data needs to be engineered into semantic features. This is a process that can be tedious and full of toil, requiring constant upkeep and often leading to rework across projects and teams. In order to reduce the amount of wasted effort and speed up experimentation and training iterations a new generation of services are being developed. Tecton first built a feature store to serve as a central repository of engineered features and keep them up to date for training and inference. Since then they have expanded the set of tools and services to be a full-fledged feature platform. In this episode Kevin Stumpf explains the different capabilities and activities related to features that are necessary to maintain velocity in your machine learning projects.
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Your host is Tobias Macey and today I’m interviewing Kevin Stumpf about the role of feature platforms in your ML engineering workflow

Interview

Introduction
How did you get involved in machine learning?
Can you describe what you mean by the term "feature platform"?
- What are the components and supporting capabilities that are needed for such a platform?
How does the availability of engineered features impact the ability of an organization to put ML into production?
What are the points of friction that teams encounter when trying to build and maintain ML projects in the absence of a fully integrated feature platform?
Who are the target personas for the Tecton platform?
- What stages of the ML lifecycle does it address?
Can you describe how you have designed the Tecton feature platform?
- How have the goals and capabilities of the product evolved since you started working on it?
What is the workflow for an ML engineer or data scientist to build and maintain features and use them in the model development workflow?
What are the responsibilities of the MLOps stack that you have intentionally decided not to address?
- What are the interfaces and extension points that you offer for integrating with the other utilities needed to manage a full ML system?
You wrote a post about the need to establish a DevOps approach to ML data. In keeping with that theme, can you describe how to think about the approach to testing and validation techniques for features and their outputs?
What are the most interesting, innovative, or unexpected ways that you have seen Tecton/Feast used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Tecton?
When is Tecton the wrong choice?
What do you have planned for the future of the Tecton feature platform?