I'm excited to share this project with you all today. It's still in beta, but with positive notes from early users I wanted to open it up to the community.
Pallet is a mobile-first machine learning platform that enables you to instantly turn computer vision models into shareable apps, and access them anytime from a single interface.
As an application developer, I was drawn to deep learning for computer vision for the seemingly magical feature of giving everyday apps the ability to "see". However, while there are a number of resources that can teach you how to build & train deep learning models for object recognition tasks, I've found far fewer resources that facilitate deploying those same models as real-world apps. There are even a handful of amazing "no-code" applications for developing image classification models, like Lobe.ai [1] or Google Cloud's AutoML Vision [2], but no comparable applications for deployment.
Moreover, given how fast the ML world has been moving the last few years, it can be a challenge to not only keep up with state of the art models, but understand how to use them in practice.
In an effort to make this tech a bit more accessible, I started building Pallet to automate hosting, serving, integrating, and updating models targeted for mobile devices.
With Pallet you can:
• Deploy custom machine learning models to mobile without code, and try them in the real world,
• Share a link to any model with one tap,
• Make predictions with state-of-the-art models anytime, and
• Explore a number of models made by the ML community
It's a first step towards simplifying the process of building, deploying, and sharing custom AI-enabled apps, particularly for individual developers, machine learning engineers, students, and data scientists.
Right now the platform supports pretty much any TensorFlow image classification model with a standard signature (including those you can export from the aforementioned platforms), and you can find the Android app in the Play Store [3]. With more time and resources, I plan to improve framework and platform support.
I'd love to hear any and all feedback in the comments, or ping me on Twitter @PalletML. Thanks!
I'm excited to share this project with you all today. It's still in beta, but with positive notes from early users I wanted to open it up to the community.
Pallet is a mobile-first machine learning platform that enables you to instantly turn computer vision models into shareable apps, and access them anytime from a single interface.
As an application developer, I was drawn to deep learning for computer vision for the seemingly magical feature of giving everyday apps the ability to "see". However, while there are a number of resources that can teach you how to build & train deep learning models for object recognition tasks, I've found far fewer resources that facilitate deploying those same models as real-world apps. There are even a handful of amazing "no-code" applications for developing image classification models, like Lobe.ai [1] or Google Cloud's AutoML Vision [2], but no comparable applications for deployment.
Moreover, given how fast the ML world has been moving the last few years, it can be a challenge to not only keep up with state of the art models, but understand how to use them in practice.
In an effort to make this tech a bit more accessible, I started building Pallet to automate hosting, serving, integrating, and updating models targeted for mobile devices.
With Pallet you can:
• Deploy custom machine learning models to mobile without code, and try them in the real world,
• Share a link to any model with one tap,
• Make predictions with state-of-the-art models anytime, and
• Explore a number of models made by the ML community
It's a first step towards simplifying the process of building, deploying, and sharing custom AI-enabled apps, particularly for individual developers, machine learning engineers, students, and data scientists.
Right now the platform supports pretty much any TensorFlow image classification model with a standard signature (including those you can export from the aforementioned platforms), and you can find the Android app in the Play Store [3]. With more time and resources, I plan to improve framework and platform support.
I'd love to hear any and all feedback in the comments, or ping me on Twitter @PalletML. Thanks!
~ Tony
[1] http://lobe.ai (or https://news.ycombinator.com/item?id=24944814)
[2] https://cloud.google.com/vision/automl/docs/edge-quickstart
[3] https://play.google.com/store/apps/details?id=com.palletml.a...