In need of labeled data, but not sure which data labeling platform is right for you? We wrote this guide to arm you with the key criteria you should consider when choosing your data labeling platform. We hope you find this useful — happy labeling!
Three Types of Data Labeling Solutions
First, let’s briefly outline the three main types of data labeling solutions you’ll find on the market today:
1. Data Labeling Tools Only (No Workforce)
These solutions provide software tools for labeling data. They do NOT provide a human workforce to label your data. You’ll either need to label data yourself (which is time-consuming and tedious), or cobble together your own workforce (which is time-consuming and operationally painful — imagine the recruiting, personnel management, and payroll headaches involved).
2. Date Labeling Workforce Only (No Tools)
These solutions provide you with a human workforce that can label your data. They do NOT provide the software interface needed to label data. You’ll need to supply that yourself. This approach is often disadvantageous — you’ll be saddled with the engineering complexity of building custom tooling for scratch or the pain of onboarding a workforce onto a tool they’ve never used.
And while these “BPO” workforces are often capable of rudimentary labeling tasks (like basic image labeling), they lack the skills necessary to perform advanced labeling tasks (writing python code, evaluating hate speech and misinformation, generating high quality chatbot conversations).
3. Integrated Data Labeling Tools and Workforce
Integrated data labeling platforms provide both labeling tools AND a human workforce. This is often the most attractive type of solution to customers, because it saves them the burden of building custom tooling or recruiting / managing their own labeling workforce.
Surge AI falls into this category — we’re a one-stop shop providing flexible, easy to use labeling tools, and a high-quality, super-vetted workforce to label your data.
How to Evaluate Data Labeling Platforms
Assuming you are interested in an integrated data labeling platform, here are important evaluation criteria for you to consider:
First and foremost, you need to ensure that your data labeling platform produces excellent data quality. This is crucial — the quality of your data has huge downstream effects on the quality of your model.
Here’s how to evaluate the data quality of a data labeling platform:
- Set up your labeling task in a spreadsheet and label 100 rows of data yourself.
- Ask the data labeling platform to run a small pilot to gauge data quality. Every self-respecting data labeling platform will happily agree to this!
- Send them your unlabeled pilot data (so they don’t know the answers ahead of time).
- When they return the data to you, compare their labels with your original labels to assess quality. If there are disagreements, discuss these with the vendor. These conversations can reveal edge cases in task design that are helpful to address. This is also a good opportunity to evaluate how well the vendor understands the nuance of your task, and to differentiate between vendors with reasonable disagreements vs egregious ones.
If you’d like to run a pilot with Surge AI, please get in touch! We’ll spin up your pilot and send you results within a week.
Though data labeling is an essential part of the pipeline for building AI/ML models, it’s a human endeavor at its core: behind every row of labeled data is a person making a determination. That begs the question — who are the kinds of folks you want labeling your data?
In other words, ask yourself:
Can my data be successfully labeled by anyone, regardless of their area of expertise, level of education, or language proficiency?
For very basic labeling tasks, the answer may be yes. For example, if you simply need to determine whether images contain dogs or cats, it’s likely that almost anyone could label your data successfully (though even then you’ll have to contend with scammers and folks who do sloppy work).
But these types of workforces (which are typically outsourced to low-cost, non-English-speaking countries) aren’t equipped to perform highly advanced labeling tasks, which may require technical expertise (to write code), language expertise (to evaluate the coherency of language model generations), or cultural awareness (to identity misinformation in American politics).
Or, do I need a workforce of educated, fluent English speakers?
Perhaps your task is generating chatbot conversations about a variety of topics, evaluating social media toxicity, or categorizing financial transactions. It’s important that the data is high-quality, but you don’t necessarily need a group of PhDs to work on the project.
In this case, you’ll likely require a workforce of fluent English speakers that hail from a variety of walks of life — engineers, students, retirees, bakers, artists, lawyers. You don’t need hyper specialized folks, but you do need smart people who think critically and embrace nuance.
Or, do I need a hyper-specialized workforce for my task?
Perhaps your task requires a hyper-specialized workforce. For example, you may need a team of Python programmers to help train your code-generation model. Or you may need a team of folks with STEM degrees to label a math dataset. Maybe you need history buffs to help train a research assistant AI.
If you answered yes to either of the last two questions, Surge has you covered. We have both a general pool of smart, highly-vetted, English speakers (we support 20+ other languages too) and niche teams (like Python programmers) built specifically for your use case.
Ease of Use
The next criteria to evaluate is ease of use. Ask yourself the following questions about each data labeling platform:
Can I communicate with my labelers?
Can I test labelers and only use the best performing ones on my task?
Can I quickly iterate on task design?
Will the platform actively offer guidance on task design and instructions?
Does the data labeling platform give me the tools I need to review and monitor data quality?
Does the data labeling platform provide an API so that I can automate your data labeling workflows?
Your data labeling platform will become an integral part of your larger production pipeline, so it’s important to know that you can integrate in a painless, friction-free way.
Speed and Throughput
It’s important to make sure that your data labeling platform can hit your speed / throughput requirements. To identify these requirements, start by asking yourself the following questions:
How many rows of data do I need labeled?
Is this a one-time project, or will it be recurring?
If it’s recurring, what cadence will I send data at? Daily, weekly, monthly?
How quickly do I need the data returned? In a day, a week, a month?
Make sure to bring these answers to your data labeling platform so that all parties are aligned on your goals and requirements.
Of course, cost will be an important factor in your decision. At a basic level, you want to understand how much the service costs, and what the breakdown of fees will look like (nobody likes a surprise on the first invoice they receive!).
At Surge, we keep it simple. You pay a flat fee per label — that’s it. No platform or setup fees, no hidden add-ons.
We hope this information is helpful as you evaluate data labeling vendors. If you'd like to see if Surge AI is the right fit for you, schedule a call with us. We’d love to meet you and discuss your data labeling needs! And even if we aren’t the right fit, we're happy to discuss best practices for data labeling and point you in the right direction.
Data Labeling 2.0 for Rich, Creative AI
Superintelligent AI, meet your human teachers. Our data labeling platform is designed from the ground up to train the next generation of AI — whether it’s systems that can code in Python, summarize poetry, or detect the subtleties of toxic speech. Use our powerful data labeling workforce and tools to build the rich, human-powered datasets you need today.