How Data Annotation Platforms Help Improve Machine Learning Models
In recent years, AI has become less of a flashy new idea and more mainstream with a wide variety of businesses implementing AI technology and machine learning models into their business practices. And, as the world generates ever-growing amounts of data, the data you need for your specific use case is likely already out there just waiting for you to claim it.
The major problem faced by companies who are new to launching AI projects is that they don’t know all of the work that goes into acquiring, preparing, and testing their data. When you first get your data, it will be raw and unprocessed. While the data has vast potential, before you can use it, it needs to be properly prepared and labeled. Data annotation platforms are what you need to get the right, high-quality data for your use case.
Choosing the right data annotation platform for your specific needs will be the key to your successful implementation and launch of AI algorithms and machine learning models.
What is Data Annotation?
Before your data is usable, it must be annotated. Data annotation is the process of labeling your data. To label your data, you can do it yourself, hire an outside data annotation partner, or use machine learning automation to label your data. Even with machine learning automation, data annotation must be supervised by a human.
To annotate your data, it must be processed, tagged, and labeled to correspond with what the data point is or shows. Data comes in a number of different formats, including text, images, and videos. Your annotation or labels ensures that the data is readable to your machine learning model.
Accurately labeled data is one of the most critical components to the success of your machine learning model. If you have low quality data or inaccurately labeled data, your machine learning model won’t be able to return accurate results. Data quality is critical.
What are Data Annotation Tools and Platforms?
A data annotation tool or platform is a tool that you can purchase, use for free, or an external partner that you hire to annotate and label your raw data prior to use. There are a number of different types of data annotation tools and platforms, the right one for your company will depend on your specific needs and use case. Many data annotation platforms specialize in labeling specific types of data or handling data that is used for specific use cases.
While there are free data annotation tools on the market, paid tools and external partner platforms are going to help you produce higher quality data, which in turn, will increase the ROI on your AI project or machine learning model.
What to Consider Before Choosing a Data Annotation Platform
When you’re looking for the right data annotation tool for your company, there are a number of different factors that it’s important to consider before you jump into an arrangement or relationship. You’ll want to find the data annotation platform that will best fit your needs and unique use case.
Data quality comes down to how accurately your data is labeled. The higher the accuracy, the better your data will work, and the higher ROI you’ll see on your machine learning model. If you put in garbage data, you’ll get garbage out.
Generally, the higher-priced data annotation tools are also the ones who produce the highest quality data. It’s important to weigh what’s more important to you: quality or cost.
Data labeling is a manual, human-led task. It requires a ton of effort and time. You’ll want to look for a data annotation tool that can guarantee you a specific accuracy rate and focuses on producing high quality data.
Before your data can be annotated, it must be compiled into a dataset. When you’re shopping for a data annotation platform, you’ll want to look at how they manage their datasets. This will become a critical part of your workflow and you want to be sure they can support the high volume of data you need annotated and can work in the file type you need. You also need to make sure that the labeled data will match your data output requirements.
While data annotation is manual and requires human intervention, it doesn’t necessarily mean it takes a long time. You’ll want to look for a data annotation platform that can return your clean, annotated data within your desired timeframes. Some companies employ a larger, more global workforce, meaning you’ll be able to get your data back more quickly.
Specific Use Cases
Each machine learning or AI project has a specific use case and data type. You might be working with text, images, audio, or video. Different data annotation platforms are optimized to work with specific types of data. You’ll want to evaluate whether or not a data annotation platform works with the type of data that you need labeled.
Specific use cases include:
Image or video
2D or 3D points
Net entity relationships or NER
Parts of speech
Audio to text
It might sound overly simple, but just like with any other digital tool or software, you’ll want to make sure that the data annotation platform you choose to work with will connect to the different tools you already use at your company. Interconnectivity is all about making your life simpler. There are a number of different data annotation platforms out there, you might as well work with one that can connect with the suite of tools you already use.
Different data annotation platforms offer different, unique features. Be sure to review the different features offered by any data annotation platform you’re interested in. What might seem like a simple feature or sales point, could make all the difference for your company.
Ability to Automate
A newer feature that some data annotation platforms have begun offering is automation of data labeling. While humans will still need to be involved in checking the automated labeling process and checking the labeled data for errors, automation can save you time and money in the data labeling process. Some data annotation projects are more viable for automation than others, so the ability to make use of this feature will depend on your specific use case.
As with any other tool, you’ll want to be thinking about how your team will communicate with the people at your chosen data annotation platform. Communication is key to the success and pace of your project. It’s important that you’ll have access to a team lead to check on the status of your project and fix any problems that come up. You’ll also want to find out what their help desk and support system looks like.
While money shouldn’t stand in the way of you getting high-quality data for your AI project, reality means you likely have a budget. You can find data annotation platforms and tools at any price point. Lower-priced platforms and tools may not return the highest quality data, but that may be your only option if you’re on a limited budget.
Before committing to a data annotation platform, it’s critical that you review their safety practices and protocols to understand what precautions they take to keep your data safe.
A couple of safety measures that you can look for in potential data annotation tools:
Limiting data annotators access to only data that’s assigned to them
Preventing data downloads
File system and cloud security
Some specific data use cases will fall under regulatory compliance requirements. If this is the case for your data, you’ll need to look for a company that can abide by these regulations. This would include GDPR, HIPAA, SOC 1, SOC 2, PCI DSS, or SSAE 16 regulation.
What to Do if You Need to Change Data Annotation Tools
Anytime you have to change tools within an organization, it’s a pain. It can have wide-ranging effects on a number of different people within your office. But, if your current data annotation tool isn’t working for you, it might just be time to make a change. If you are looking to change tools, be sure to make notes on what you don’t like about this current tool so you can look for a tool that will solve those problems.
When comparing new data annotation tools to your current set up, you’ll want to evaluate:
How data is uploaded
The resources and training offered by the data annotation platform to teach your team how to use it
Data storage and security
Quality assurance for data annotators productivity
There are a number of different data annotation tools out there and it’s important to periodically review the options available on the market. You might find that a new tool has been introduced in the last year or two that better fits your needs and specific use case.
How Appen Can Help Your With Data Annotation
If you’re looking for an external data annotation partner and platform, Appen just might be able to help. Our focus is always on producing high-quality data efficiently for our customers. We offer data annotation software, SAAS products, and managed services so you can find just the right fit for your annotation needs. While we do offer automated data annotation, we always keep humans-in-the-loop to ensure accuracy and efficiency.
With a distributed crowd of over 1 million data annotators in 170 different countries and expertise in 235 different languages, we have one of the best and largest data annotation platforms in the world.
We offer data annotation services such as:
Test, sensor, and audio annotation
No matter your labeling needs, we have the technology, crowd, and industry expertise to help you collect, classify, annotate, transcribe, and translate your data. We also guarantee high-quality data through our Smart Labeling technology.
Our Smart Labeling suite of tools uses machine learning assistance to annotate data automatically, which improves productivity, quality, and delivery speed. Our machine learning assistance combines machine predictions with human annotators so you get your data faster with no sacrifice in quality. Our Smart Labeling tools focus on three specific areas: pre-labeling, speed labeling, and smart validators.
Our pre-labeling tool uses machine learning automation to provide a “best guess” hypothesis as a data label. Then, human contributors review pre-processed annotations instead of starting from scratch. This drastically reduces the amount of time spent on each task.
Our speed labeling cognitive strain for contributors, which increases speed and comfort, while making the tools more efficient to use.
The brilliance of our Smart Labeling suite of tools is how machine learning and human contributors work together to get the best final product. Our smart validators tool uses machine learning to verify human judgments before they’re finalized. This eliminates the need for peer reviews and ensures that you still get the highest-quality product.
Variety of Use Cases
At Appen, we work with a number of different customers on a wide variety of different use cases. Some of our most popular machine learning-powered annotation tools work on:
Video object tracking
Image data labeling
Text utterance collection
At Appen, we are proud to offer some of the highest levels of data security available to our customers. This includes meeting data security requirements for clients that work with personally identifiable information (PII), protected health information (PHI), and other sophisticated compliance needs.
No matter what type of AI project or machine learning model you’re working on, data annotation will be critical. Collecting data just isn’t enough. You’ll want to work with the highest quality data to ensure that you’re getting the best results from your algorithm. Choosing the right data annotation platform or tool to help you on that journey will be a big decision for your company.