7 criteria for choosing the right Data Labeling platform
💡 The quantity of platforms of Data Labeling on the market has never been more important. There are a multitude of technological solutions for annotating data and producing datasets (”Training Data“) that will feed your artificial intelligence models.
However, Data Scientists sometimes tend to overlook their Setup technological (”I use LabelImg and it has been working for years, why change the environment?“) while it can directly influence the results of the models, in an AI approach centered on data.
🧐 So what are the aspects to consider before choosing your Data Labeling platform (or Training Data Platform)?
1. User interface of your Data Labeling platform
It is important that the interface is intuitive and easy to use by Data Labelers. Verify that the platform offers a clear and simple interface, which allows you to work quickly and efficiently. La Responsiveness of the interface is also a criterion, as well as the possibility of configuring keyboard shortcuts that will save your Data Labeler team valuable time...
2. Data labeling features
Verify that the platform you choose meets your needs and requirements in terms of functionalities, and in particular annotation types that you are looking to achieve (Image Labeling or Video Labeling using Bounding Box, Polygon, Keypoint, Polyline, Semantic Segmentation,...). Another feature that is often overlooked is the ability for the administrator or Labeling Manager to accurately monitor the activity of Data Labelers...
It is also a good idea to consider the existence of features of Active Learning embedded in the platform. As a reminder, Active Learning is a machine learning approach (Machine Learning) in which a learning model is trained interactively, selecting the most informative learning examples to improve its performance. Some solutions on the market such as UBIAI (NLP annotation solution) include this functionality, which makes it possible to present pre-annotated data to a human expert (the Data Labeler) and to progressively enrich the training data set... and therefore to improve the efficiency of the process of processing your labeling tasks!
3. Data import and export functionalities and the format of extractions
Some platforms make it possible to extract labelled data in a standard (JSON) or specific format (XML, TXT, YOLO,...) with varying degrees of success. For some free solutions, data is sometimes “lost” during the extraction process, a process that can also be very time-consuming because it is not optimized. It also happens that the data import process is not very intuitive (case of CVAT, the use of which is particularly complex when you want to import pre-annotated data). These are all key points to check before adopting a new tool!
4. Support offered by publishers of Data Labeling solutions
It is important to ensure that the Data Labeling platform offers a quality support. Do not hesitate to check that the publisher of the labeling solution (SaaS or on-premise) has a team dedicated to the support and requests of users of the AI annotation solution.
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5. Costs (Data Labeling platform license fees and costs incurred by using Data Labeling Outsourcing)
Finally, don't forget to compare the costs of different Data Labeling platforms. Many of them are free at first glance, but some features represent hidden costs for your business. Some platforms have a free trial version up to a certain volume of data... with rewards, namely limited functionalities or conditions of use/ownership of your data! Make sure you choose a platform that suits your challenges but especially your budget!
Finally, some platforms offer services of provision of Data Labelers on demand... The approach is commendable, but find out how are sourced the Data Labelers made available (are they internal teams, teams Crowdsourced, a partnership with an AI and Data Labeling outsourcing specialist such as Innovatiana,...). It is generally a subcontracting process at the initiative of the publishers of labeling platforms, and the transparency should be in order!
6. Hosting your data (Cloud storage) and security
It's always tempting to use a SaaS Labeling platform to speed up your labeling process. But think about your data as well! Some publishers offer a secure environment and “guarantees” (ISO27001 certification, SOC2 report,...) where others offer trial versions that seem attractive at first glance, with a quid pro quo: you lose ownership of your data beyond a certain volume! Remember to read the conditions of sale carefully before taking out a contract, paid or not, with a labelling platform. Of course, this does not apply to all use cases (some raw data or free datasets obviously do not require particular attention to data confidentiality).
7. Finally, don't forbid yourself from using several AI labeling platforms!
In an approach”Data-centric“of AI (Machine Learning & Deep Learning), if the data quality is essential to obtain good results, the Data Scientist should prioritizeuse of a multitude of platforms depending on the use cases. We don't do NLP the way we do Computer Vision - to date, there is no perfectly ergonomic solution for all your developments. It is therefore up to you to define your own Data Labeling strategy and this must involve prior reflection on the tools!
💡 TLDR : in summary, to choose your Data Labeling platform and prepare your Machine Learning data in good conditions, it is important to consider the user interface, functionalities, extraction format, support, and costs ! You should also consider the nature of your use case (Computer Vision, NLP, LLM, etc.). Do your research and take the time to compare the different options to find the platform that best fits your needs. We have tested a multitude of platforms and can help you, do not hesitate to contact us!
