Introducing the Fritz AI Dataset Collection System

Collect user-generated data for actionable performance insights, model retraining, UX improvements, and more

Today we’re excited to announce the Fritz AI Dataset Collection System, an important piece in our effort to help ML teams and developers generate, collect, and use data for mobile machine learning projects.

Data is a critical component to AI workflows, and the best data comes from the real world, where your models are (or will be) deployed.

For mobile machine learning projects, that means a user’s phone. The new Dataset Collection system makes it easy to gather this data through the same convenient SDK that delivers and runs your models.

With this capability, developers, ML teams, and product managers using Fritz AI can obtain new data for model retraining, actionable insights into model performance, and learn how users are interacting with their apps’ ML features.

The Fritz AI Dataset Collection System and the Dataset Generator give developers the data tools they need to create production-quality mobile machine learning models.

What the Dataset Collection System does

Developers need to know both what their models predict and what their users expected. The Fritz AI Dataset Collection system captures both — with this functionality, engineering teams can better understand real-world model performance and usage.

Specifically, the Dataset Collection System builds a data feedback loop between engineers and app users, which helps ML teams do the following:

  • Increase visibility into practical model performance in the wild.
  • Collect new data that more closely matches real-world usage.
  • Create new datasets for model retraining and improvement.

And for those managing mobile ML projects, the Fritz AI Dataset Collection System helps to:

  • Provide actionable UX insights with data that reflects real-world usage.
  • Speed up the project development lifecycle from beta to production-ready applications.

Currently, the Dataset Collection System works with 2D pose estimation use cases on iOS only. But we plan to support more machine learning tasks in the future, so let us know if there’s a specific use case you’d like to see supported.

How the Dataset Collection System works

The Fritz AI Dataset Collection System enables developers, ML teams, and product managers to:

  • Collect and easily browse new, annotated data generated by end users (with their permission, of course).
  • Understand model predictions made in real-world scenarios.
  • Review and approve/adjust user-generated annotations.
  • Generate new, annotated datasets from collected data, important for model retraining.
  • Target model improvements that address specific usage scenarios and edge cases.
  • Enhance an app’s UX by gaining actionable insights into how users behave and engage with ML features.

Here’s a link to a quick explainer video that shows the Dataset Collection System in action:

Getting Started

Fritz AI Studio, including DCS, is now available to all developers and teams. If you have a mobile machine learning project that would benefit from this kind of data collection, we’d love to hear from you.

For more complete information, check out the official documentation.

As always, if you have any questions, don’t hesitate to reach out.

Happy Building,
The Fritz AI Team

Fritz

Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

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