ML Backend, Pre-Label and Auto Annotate

Introduction

Conceptually there are the following major approaches:

Pre-Label
Interactive (Front end)
Workflow
SDK Model Wrapper

Pre-Label

You can use your model to pre-label data. This means predictions will show up in the Diffgram UI. This can be used to review data, improve data, and more.
There are multiple ways to send that data to Diffgram.

You can use the import wizard or the SDK or REST API
You can even update existing files multiple times.
And you can store multiple model prediction version on the same file and compare them.

Learn more Importing Introduction

Interactive

JS or via Realtime API call
Userscript Examples

Workflow

Integrate ML backends in an event driven way. Workflows

Also Known As

Model Assisted Labeling (MAL)
Auto Annotate
ML Backend
Active Learning
AI Assist
Prelabel
Pre-Label
Data Centric AI

Active Learning

Active learning is when the input from the human effects the AI model. Some people feel this must be "interactive" e.g. real time updates to the model. Others see it more broadly as any type of iteration loop, even if it's over a period of days. Diffgram supports a wide variety of active learning methods, and we are continuing to add more.