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Top 5 Factors To Consider When Looking For A Data Labeling Platform

In this article we’ll look at some of the top factors to keep in mind when deciding on which data annotation platform to use. (Also called Data Labeling)

All of these factors are very important and can make or break your AI supervised labeling projects, so let’s keep them in mind when checking all the available options.

Data Labeling Platatform: Key Factors

1. Good Data Management Tools On Your Data Labeling Platform

When working on a data management platform or any labeling software, you’ll spend a lot of your time viewing files, copying them and exporting them into JSON, YAML or TF Records formats.

One of the most recent trends in management of training data is the pipelines architecture. The idea is that you define workflows on your data to move from one folder to another as different labeling stages are completed.

This will greatly reduce communication time, and file management in your project as you will automatically receive all completed files in an single folder in real time.

Make sure that your data labeling platform allows you to:

  • Create Data workflows
  • Assign tasks and automatically move them to completed folder.
  • Support webhooks or any other export mechanism to automatically send your labeled data to your model creation functions.

2. Labeling Accuracy and Strong Quality Assurance (QA) Mechanisms inside the Data Labeling Platform

You need to make sure that your labeling team is doing a good work, so usually you’ll hire some domain experts to double check the labels. Make sure that they are not sending screenshots or PDF documents via email, because this will create a mess and all the information will be scattered everywhere.

Your data platform should support strong QA mechanisms where you can easily pinpoint an issue and automatically share it to your teammates, just like you do on Github.

Allowing to go to the file and video with a couple clicks, and discussing the labeling process is important to keep information centralized on your labeling platform, increase your labeling accuracy and reduce overall labeling time.

3. Integration Capabilities of your Data Labeling Platform with your Internal Tools

All your labeled data is worthless if you are not able to easily import it into your other ML and AI tools for model building and evaluation.

The best way to get the most flexibility in your data pipelines is that your labeling platform comes with a strong SDK so you can have all the flexibility you need to access the data whenever you want at any stage of your Machine Learning Pipeline.

Another great tool is the ability to support webhook events so you can get notified when important things happen on your labeling platform. For example:

  • You may want to know every time a labeling task is completed.
  • You may want to know when an entire batch of tasks are completed.
  • You may want to know when new files are added an new labeling tasks are available.

Having webhooks will allow you to know about this, and take appropriate actions when any of theses events happen. Here’s a great SDK example from one of the most popular labeling platforms.

4. Strong Video Support Inside The Data Labeling Platform

If you are labeling video data, make sure your data labeling platform allows you to scale as your video data grows.

You should be able to decide which specific frames you want to label without having to do any pre-processing yourself. Being able to upload anything from a 1 MB video to large 30–40GB videos. And to manage them seamlessly is important as your data usage grows and your project becomes bigger.

Another important aspect of video support is the ability to quickly label multiple frames quickly. You should check for features like instance templates, copying and pasting to multiple frames and even automatics labeling with interpolation or any other mechanism.

5. Security and Good Cloud Integrations within the Data Labeling Platform

Many tech companies now rely on either AWS, GCP or Azure for their infrastructure, specifically, for their data storage. Secure data labeling enables you to control how and where the data labeling platform is installed.

Services like S3 or Google Cloud Storage are really useful to keep your data safe and at low costs of storage. You labeling software, should allow your to import directly from these providers and export back the results of your labeling tasks in an easy way.

With all of the above factors in mind, you’ll surely make the best decision when choosing your next data labeling software.

What’s your favorite labeling Software? Let us know if the comments!

Are you working on an AI Data Annotation project? Contact Us today!

Diffgram is one of the most complete and well rounded data labeling platforms offering strong video support, cloud integrations and even on-premise installations with Kubernetes.

Keywords:

data annotation, data annotation tools, training data platform, labeling platform, ai training data platform, data labeling platform, fully managed annotation, data collection, data platform for ai, ai data platform, ai training data companies, annotate online, data annotation jobs, labeling pricing, semantic segmentation, video annotation, image annotation, image labeling services

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Supervision for Machine Learning. Training Data. (AI/ML/DL)

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Pablo Estrada

Pablo Estrada

Full Stack Developer

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