5 Questions to Help you Choose the Right Data Labeling Tool
The versatility of a data labeling tool can make or break your data quality. And the data quality can make or break your algorithms. And what happens when our algorithms misinterpret or fail? — Karthik Vasudevan, Founder at Traindata Inc.
This post will guide you to ask five questions to help you choose the best data labeling tool.
1 — What’s your use case?
The first factor you should consider while choosing a data labeling tool is the kind of data you want to label. Image data annotation is different from text or video data annotation.
Data labeling types such as bounding boxes, polygon, 2-D and 3-D point, semantic segmentation, etc., demand a specific kind of software to label data.
Another way to differentiate data labeling tools is by annotation features, QA, supported file types, data security, storage options, and more.
As data labeling tools are priced differently, your project budget will also influence your decision-making.
What to do?
List down the following factors:
Analyze the type of data that needs labeling.
Analyze the type of annotation required.
Your data storage and security requirements you want in the data labeling tool.
And the quality assurance (QA) features you need.
These factors will help you narrow down your choices.
2 — Build or buy?
Unique labeling cases require equally special data labeling tools. While ready-made labeling tools are developed and iterated to match any data labeling need, every data labeling project can still find certain things the tool cannot or won’t manage.
If you have a strong development team, then you may consider building a bespoke labeling tool.
The advantage in developing your tool is that you can incorporate your security controls, support system and scale the tool per your requirement.
But the reality is that it takes nearly six months to build a data labeling tool from scratch.
What to do?
If your data labeling is a unique case, that deals with sensitive data, and you have the budget to build, maintain, sustain, and scale the labeling tool, making one might be your best choice.
Here are some advantages of building your own data labeling tool:
You can build the tool around your data labeling process. While most data labeling tools are flexible, no third-party tool can 100% match your workflow.
You get better control to label the data so that you can eliminate bias in data annotation.
Update and scale the tool when you need.
Gain tremendous control over security features.
And, no need to get locked with one data labeling provider and the tools they offer. By building your tool, you can hire and train data labelers on the tool and help them seamlessly plug into your workflow.
3 — What kind of company are you?
Are you an early-stage company that relies on open-source tools to build your platforms, or are you an enterprise operating at scale?
If you are the former, you may choose to hire a crowdsourcing partner to label your data and keep your costs down. Crowdsourcing comes with its challenges where you don’t get to meet or know your data labelers.
If you are an enterprise operating at scale, open-source tools are a better choice.
You’ll likely have long-term core processes and stack integration that provide maximum control over security as well as the agility to make changes.
At this level, if you’re using commercial software, you typically can get bespoke tooling that is fully customized for your needs and doesn’t require heavy development resources.
What to do?
Let the evolution and the current stage of your organization guide you to choose the best tool and the data labeling workforce.
4 — The workforce vs tool dilemma
Here’s a choice you have to make — choose a tool that works well with your labeling team’s skill and experience level or pick the tool first and then hire and train labelers.
While the skillsets of your data labelers are what defines the quality of data labeling, it is the versatility of the labeling tool that empowers and enables your labelers to do quality annotation. It would help if you struck a balance between both — labelers and the tool.
What to do?
Get your workforce to adapt to your tool quickly. Create a strong, closed feedback loop with your data labelers. Why? As their familiarity and context with your data grow, data labelers will bring valuable opportunities for you to streamline your process.
5 — What’s your QA process?
Many ready-made labeling tools come with extensive QA features where you can automate some parts of QA.
QA automation helps you speed up your labeling process. However, even when using time-tested automation for a portion of your data labeling process, you will need people to perform QA on that work.
What to do?
What you need is a human-in-the-loop approach to QA. Even if you choose to build your labeling tool and label the data in-house, you may want to hire a data labeling partner exclusively for the QA process.
Data labeling partners have highly trained and experienced data labelers and QA specialists, and choosing to get an experienced team to quality-check your labeling can deliver high-quality data.
Traindata is built by ex-Yahoo!s with over 15 years of experience managing and preparing data for large-scale ML projects. Visit www.traindata.us to hire us to label your data.
P.S: This blog post originally appeared on traindata.us/blog
