AI-assisted Data Labeling vs Manual Data Labeling
You often look at the amount of data you collect and tell yourself “what if we could look at our data and understand our business/customers at a more granular level and see if we could enhance or create new services or products?”
When you arrive at this moment, you immediately think of three things:
- Big data — leverage data and feed it to machine learning algorithms.
- Artificial intelligence and Machine learning — to build algorithmic models to see patterns and build solutions.
- And data science — to analyze, identify, and visualize value from your data.
As you decide to leverage data and build AI/ML solutions, you prepare the data and structure it to feed it to your machine learning algorithms.
If you haven’t got enough data, you may choose to acquire it before preparing all the data and handing it over to your machine learning engineers.
Just before you hand over the data to your engineers, you need to structure the data, label it, annotate it and hand it over to your engineers in three phases:
- A set of data to train the machine learning algorithms.
- Another set of data to test the trained-algorithms,
- And a set of data to validate the algorithms.
What is manual data labeling?
Running an AI/ML project needs great vision and deep pockets.
Nearly 75% of the resources is consumed in identifying, collecting, collating, and structuring the data required to train, test, and validate your ML models.
As a result, many businesses choose to outsource data labeling and preparation activities to developing countries with low labor costs to work within the limitations of the AI/ML budget.
However, data annotation and labeling needs equal amounts of skill and experience to provide a high quality labeling output.
Sometimes, you may request to hire professionals in a specific field as labelers. The medical industry is one such example.
Obviously even trained labelers need time to get acclimated with your labeling conventions and standards, and this adds time to your overall project timeline.
One way to speed up data labeling is to use AI-assisted labeling tools.
What is AI-assisted data labeling?
As human data labelers label a small set of data, an AI-assisted data labeling tool learns the patterns and conventions of data labeling and can independently start labeling data at a tremendous speed.
With a bit of supervision, you could get an AI-assisted tool and a bunch of data annotators to label all your data in quick time.
3D annotation and video annotation are considered as the toughest services in data labeling. At present, object tracking algorithms based on machine learning have already assisted video annotation.
The annotator annotates the objects on the first frame, and then the algorithm tracks the ones in the subsequent frames.
The annotator only needs to adjust the annotation when the algorithm doesn’t function well. It is 100 times faster than before.
You get two major benefits by using AI-assisted + human data labeling:
- Cost reduction: With the help of AI-assisted capabilities, you can save more money as the labor cost goes down.
- Time reduction: Make the large-scale requirement of training data done in a short time. Using AI-assisted tools can improve efficiency multiple times.
Can you eliminate human data labelers and depend completely on AI-assisted labeling?
The answer is no.
In fact, manually labeled data is less prone to errors regarding quality assurance and data exceptions.
The human workforce cannot be totally replaced by some tools leading with an AI-based automation feature, especially dealing with exceptions, edge cases, complex data labeling scenarios, etc.
The fear of a sliver of bias and misinterpretation derailing the entire AI/ML project is very real and can be an expensive mistake.
What is the best, economical way to get your data labeled at high quality?
The answer is simple = choose a data labeling vendor who offers manual and AI-assisted data labeling.
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 karthikv@train-data.com or visit www.traindata.us to learn more.
P.S: This blog post originally appeared on traindata.us/blog
