Best Data Labeling Software 2022 • 5 Data Labeling Softwares Reviews
Data labeling software is an extremely useful tool for empowering machine learning. That’s enough of a reason for us to examine the top solutions on the market today.
Data Labeling Software Leaderboard 2022
What is data labeling software?
Any serious machine learning project depends on the amount of data it can analyze. It needs the data so it can “learn.” However, machine learning models require certain labels attributed to data.
People use data labeling software to identify raw data for the machine learning model. The raw data includes files such as text, videos, audio, and many others. Now that we are familiar with what these tools do, let’s learn more about how it all works.
How does data labeling software work?
Currently utilized machine learning models rely on the method known as supervised learning. By using the model training process, a machine learning model picks up various patterns recognized in the labeled data. But what is the role of data labeling software, you might think? Well, to support the aforementioned processes, it is first necessary to label all the data. This is where humans interfere and use the data labeling software to label all the relevant raw data.
For example, to create a machine learning model that can recognize an image that includes a car, thousands of images containing automobiles have to first be labeled as such. A broader type of labeling would only state whether there is a vehicle on an image. To improve the machine learning process, the labeling can be performed pixel by pixel. Yes, it is as arduous as it sounds, but the machine learning models become much better at making predictions when faced with raw data in the future.
Advantages & Applications
Fueling the machine learning process requires a set of labeled data. Streamlining this process is the biggest advantage that data labeling software tools provide to companies. Furthermore, these software tools come with collaboration support, meaning that teams of people can work together on labeling data. These advantages make these tools essential for streamlining workflow in companies across numerous industries.
For any type of company, implementation and use of data labeling software ensures that a business remains competitive. Artificial intelligence (AI) is continuously growing because labeled data power it, and business owners have to use these software tools to remain competitive. It is the biggest advantage provided by these software tools.
When it comes to applications of data labeling software, it is very broad. In any industry where some sort of machine learning for AI development is used, data labeling software is going to be used. Whether we are talking about image machine learning for autonomous driving or healthcare X-ray–reading AI, labeling software is going to find its role in the whole process. The same goes for text-, video-, or audio-related AI development.
What types of data labeling software are there?
There are two ways in which data labeling software can be categorized. One is more relevant regarding costs, while the other is based on the type of data the tools can label. Let’s examine the cost aspect.
When it comes to costs, data labeling software tools can be one of the following.
While the proprietary data labeling software does come with annual expenses, you get the team behind the tool to help you set it up and start using it for your business.
When it comes to types of data that can be labeled, these software tools differ in the following ways.
- Natural Language Processing (NLP)
- Computer vision
Each of these data types is labeled in specific ways. The process does not only depend on the data type, but also the features of a data labeling software. For example, while labeling images requires labeling key points and pixels, texts will require selecting parts of the text to create the training dataset. When it comes to audio, it will require transcription to help with audio categorization.
All these differences are why these software tools are tested according to various criteria. Let’s explore how data labeling software test winners are awarded. There are several things that tests consider.
How Data Labeling Software is Tested
Given the important role of data labeling software tools, it is no wonder that many online reviews thoroughly examine them. Many test them according to several criteria.
- Features – What features does the software tool provide?
- Complexity – How hard is the software to use?
- Performance – Does the data labeling software perform smoothly?
- Collaboration – Are the collaboration features available? Ideally, you want several people working on the labeling process simultaneously.
- Pricing – Is the tool affordable?
Answering these questions is essential for determining a winner in any data labeling software test.
What to Look for When Buying Data Labeling Software
Examine your needs
What kind of data labeling are you planning to do? That’s the first question to answer. It will help you make a better decision when looking for data labeling software.
What company is behind the product?
Consider opting for a company that has been present in the market for some time now. Chances are that its product performs well, and you can count on future updates.
What features are supported?
Explore data labeling software features in great detail. You want to know exactly what you are going to get. Doing this will let you make a list of top choices for your use case.
Evaluate the pricing of each software tool. Opt for the one that offers the most value. The price is tied to your needs, so make sure that you get them right. Choosing the product with the highest value will be easy.
7 Leading Manufacturers and Their Leading Products
- Amazon SageMaker
- Apache Hive
- V7 Darwin
Internet Versus Retail Trade: Where should I buy data labeling software?
Note that these are software tools, meaning that the majority of them work in the cloud, which requires the internet. Visit a brand’s website to see their pricing and purchase the software there.
Advantages and Disadvantages Based on Customer Reviews
- Pricing – For many customers, some data labeling costs are simply too high. The problems always come down to labeling a larger amount of raw data.
- Challenging configuration – Many customers across various platforms experience challenges during the setup phase of the data labeling tool. However, once finished, using the software is smooth sailing.
- Steep learning curve – Many of these software tools have a steep learning curve. Many customers have shared that they needed quite a bit of time to understand how to use the platform.
- Lacking documentation – Some of the brands do not include any documentation or instruction for beginners. This is a frequent issue that many people complain about for various brands.
- Incomprehensible documentation – While some don’t include documentation at all, others make documentation that is hard to understand. As customers have reported, some documentation is written by developers, for developers. It might take some time to decipher it.
- Too many features – Some customers complain that there are many unnecessary features in some of these software tools.
- Not all mediums are equally supported – While some tools advertise that they offer text, image, video, and audio labeling, not all of these are supported equally well. Our suggestion is to check reviews of the brand you are planning to use in case you need all these data labeling features.
- Customer support issues – Like in many other industries, customers express their disappointment in customer support quality for some brands. Not only do they reply days after being contacted, but they are also not able to immediately help the customer.
- Late updates – As this is a rapidly-developing industry, regular updates and bug fixes are expected. Some brands take a lot of time to update their products.
- No online community – For the majority of tools you opt for, finding an online community such as a forum will be challenging. It might be better to immediately reach out to customer support.
- Ease of use – There are plenty of user reviews describing tools as easy-to-use. This can vary from one product to another.
- Regular updates – Many customers have stated that their software tools are regularly updated. This ensures that the software is more stable.
- Introduction of new features – The introduction of new features brings more value to the product. This is an important advantage of using the right software tool. Value accumulates over time.
- Pricing – Unlike many who have shared that pricing can be tricky, many reviewers state how the pricing of the data labeling software is reasonable.
- Detailed documentation – While some brands lack documentation, others offer instructions that help their users start using their tool in a short period.
- Outstanding customer support – Many customers have shared that their customer support experience is outstanding. Find brands that offer good customer support—you never know when you might need it.
- Intuitive user interface (UI) – Some customers have shared that certain data labeling software tools have an intuitive UI, which helps them with their workflow.
- Onboarding – Some brands go the extra mile by offering an onboarding training session for their software tool. We suggest you find out which brands offer this; it will help you and your team to better understand the tool.
- Features – The wide array of features is something that many customers appreciate. It simply adds more value to their purchase.
- Versatility – Many customers deploy software tools that can label text, images, videos, and audio data. When it’s present, this feature is widely praised.
Interesting Facts & Advice
History of Data Labeling Software
Data labeling software follows the path of artificial intelligence (AI) growth. John McCarthy first coined the term artificial intelligence back in 1956. Over the decades, AI development and its stable growth have indicated that it is here to stay. More importantly, it is now a booming industry as machine learning is used in various industries.
With its perpetual growth, the further advancement of AI relies on data. This is where data labeling software has made its appearance on the market, some twenty years ago. Nowadays, there are many competitors in this growing niche.
Future Figures about Data Labeling Software
It is safe to say that in 2020, the market value of machine learning and AI across various industries has matured. More importantly, there is only more growth in the coming years, as the forecast from 2017 to 2025 predicts a steady growth rate of 36%. But what does this all mean for data labeling software?
But what has made all this possible? The main reason for expansion lies in the fact that machine learning and AI technologies have become more affordable. Medium-sized businesses have started using it to further improve their performance.
Implement Data Labeling Software in 3 Steps
What is data labeling?
Data labeling is the process by which raw data is labeled for machine learning. Once the data is labeled, a machine learning model learns repeating patterns that it can recognize and implement on future raw data.
Why data labeling is important?
Data labeling provides a foundation for the machine learning model. The more accurate the data labeling is, the better the model will work in the future.
How can data labeling be learned?
It depends on the data labeling software. Every tool has its data labeling methods. Find a tool and give it a try.
How is data labeling done?
How you label data will depend on the data labeling software you use and what type of data you are labeling.
Is data labeling software easy to use?
It depends on the software; some tools are easier to use than others. However, those with a steep learning curve might be more rewarding in the long run.
What data can be labeled for machine learning?
Raw data that includes text, images, videos, and audio can be labeled.
Is data labeling important for machine learning?
How many data labeling software tools are available on the market?
There are currently over ten different data labeling tools and platforms available. Given how quickly the market is growing, there will likely be more available in the future.
Is machine learning data labeling expensive?
It depends on how much data needs labeling. The higher the number goes, the higher the price is going to be.
Can data be labeled for Natural Language Processing (NLP)?
The short answer is yes. Labeling text is possible for Natural Language Processing machine learning.
Further Links and Sources
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