There are three ways to get your data labeled for your machine learning projects:
- Get your data labeled in-house by your employees.
- Or through a crowdsourcing company.
- Or through a data labeling partner who has trained staff on their payroll.
Data labeling and annotation could be laborious and time-consuming for your in-house resources.
Choosing a data labeling partner might be the best choice to get your machine learning project off the ground.
This post is a simple checklist guide to help you choose the best data labeling partner.
Step 1: Define your goals.
Start by setting your project expectations and document it as a project overview.
Create a document and list your project goals, budget, and timeline.
Your goals and budget should determine how much data you need to train, test, and validate your machine learning models.
Step 2: Evaluate multiple vendors.
There are three types of data labeling vendors:
- Ones who offer just data labeling platforms.
- Those who provide a pool of qualified, skilled data labeling workforce who can annotate and label text, image, audio, and video data.
- And ones who offer both.
To ease data labeling requirements, you may choose a data labeling partner who offers both the tools and highly trained and skilled labelers.
Evaluate vendors on previous data projects, the skill level of labelers, processes in place to secure your data, and pricing.
Step 3: Request a proof-of-concept.
Once you choose a data labeling partner, you should run a pilot test project.
This pilot is a bite-sized chunk of your total data labeling requirement, and the vendor should oblige to run a test pilot.
The pilot allows your data labeling partner to warm up his skilled staff to the data labeling conventions, speed, and accuracy.
Both you and your data labeling partner can review, tweak, and iterate labeling parameters and output during the pilot.
Once you are satisfied with the outcome of the pilot, it is time to begin labeling all the data.
Step 4: Establish communication standards.
Open and transparent communication with your labeling team is essential.
Based on what you are observing in model testing (pilot), validation, and implementation, your annotation service provider must have the ability to adhere to instruction sets and adapt data labeling to make improvements as you iterate.
Quality assessment should be a routine with methods such as Gold standards, Consensus, and random sampling.
Step 5: Iterate and scale.
You should regularly (daily) monitor data labeling quality and speed post-pilot.
If you find opportunities to improve the quality of work, this is the time to make iterations and course corrections before you scale to full speed.
Step 6: Consider fluctuating data labeling demand.
The data labeling requirement may fluctuate as your machine learning engineers train, test, and validate models.
Your data labeling partner should be capable of putting more labelers when you need more data and reducing headcount when demand decreases.
Step 7: Flexible and transparent pricing.
As your data labeling requirement fluctuates, your vendor should be capable of estimating demand through the model training, testing, and validation phases and offer a transparent pricing structure.
You do not want to encounter surprise costs in the middle of the labeling project.
Industry pricing could range from pricing-per-annotation
to a one-time tool cost.
We recommend man-hour-based pricing,
where it is easier for you to estimate the cost of data labeling as you scale precisely.
Aim for a pricing model that suits your data labeling demand, and you pay for what you need.
Step 8: Workforce training and stability.
The foundation of delivering high-quality data labeling and annotation is to have well-trained staff with little-to-no employee churn.
Your data labeling partner should be capable of sustaining their workforce and deliver high-quality data labeling consistently.
We are Ex-Yahoo!s with over 15 years of experience preparing data for AI/ML modeling.
We have trained staff who are good at labeling and annotating any form of data.
Get your data trained on time and budget now.
Tell us your data labeling requirements at [email protected]
or visit www.traindata.us
to learn more.