What is labeling data for machine learning all about how it’s done? If you do not have a clue, you’ve come to the right place.
It’s perfectly normal if you’ve never heard of data labeling for machine learning or the like. Data labeling is a highly technical term commonly used by the AI/machine learning and the language industry. But you must have experience with computer vision technology or natural language processing in one way or another. For example, operating a self-driving vehicle that auto-detects objects or engaging with a chatbot in a conversation on a shopping site to get your required information.
Read on to learn more about the definition, approach, and process of data labeling.
What is Data Labeling for Machine Learning?
Data labeling, also known as data annotation, refers to the process of tagging target attributes to training data for machine learning models. Data labeling identifies the raw data (generally in the forms of texts, images, videos), and then adds one or more labels to these data so that the machine learning model can make the expected accurate predictions based on the context provided by the labeled data. This is the preprocessing stage that prepares label data for the development of a supervised machine learning model.
Computers depend on data (labeled and unlabeled) to complete the training for machine learning models. While labeled data is used in supervised machine learning, unlabled data is used in unsupervised machine learning. Compared with unlabeled data, labeled data is more time-consuming to acquire and store and, thus is more expensive.
Data labeling makes possible a wide range of machine learning (ML) and deep learning (DL) use cases. The most familiar examples encompass computer vision and natural language processing.
How to Label Data for Machine Learning?
Labeling data for machine learning models is never as simple as it seems. To ensure the high performance of a machine learning model, using the best data labeling approaches is key. The selection of the best approach is up to the complexity, scope, and size of that task, as well as the budget and time the company has to implement the project. Here are some data labeling approaches and their pros and cons:
Internal labeling (In-house labeling)
This approach uses an internal data science team to simplify process tracking and ensure the highest accuracy of the labeling data. Higher accuracy means this approach takes more time to perform. If your company has enough time and financial resource, as well as an access to extensive data labeling resources, you can consider internal labeling.
Outsourcing to Individuals or Companies
Hiring freelance data labeling individuals can also expedite your machine learning labeling project. However, it takes a considerable amount of time to develop and manage a freelance team which members may or may not have the required data lableing background or experience. Before there’s even a team, you have spent plenty of time recruiting on social media platform trying to vet the right candidate. Not to mention the time costs and efforts to be put in training the team to use a specific data labeling tool.
This is why when it comes to outsourcing AI data labeling service, you should always prioritize working with data labeling companies which are fully familiar with data labelling process and specialize in training the data annotation experts to prepare the data for machine learning. They can focus on advanced data labeling tasks while also deliver and guarantee the high quality of the labeled data.
Synthetic labeling uses algorithms or computer simulations to generate annotated information (synthetic data) so as to improve the data quality and project efficiency. However, this approach is demanding of computing power and hence it’s more expensive.
If you aim for less time consumption and zero human annotation, data programming is the go-to option. This approach enables a model to utilize the skills and knowledge of human annotators and domain experts through scripts. Data programming creates high-quality datasets for machine learning model training in the most scalable, adaptable, and governable way.
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Data Labelling Process - How It Works
No matter what approach you opt for, the data labelling process progresses chronologically as follows.
The collection of the required amount of raw data takes place at the beginning of every machine learning project. Obtained from various sources, the raw data, whether texts, images, audiovisual files, etc., is either inconsistent or unsuitable for data training, which means data cleansing or preprocessing is not an option but a necessity before creating any machine learning label. Generally, a model depends on a tremendous amount of diverse data to generate higher accuracy results. The larger the amount and the higher quality the training data, the better the results.
The labeling of the collected and preprocessed data is a big deal in the labelling process. During this step, the data annotators run over the data and attach context with a specific meaning for an ML model to use as target variables for expected predictions by adding metadata tags, such as the description of the depicted objects in images, to these data.
Data Quality Assurance
The data labeling for machine learning training must be accurate, consistent, and reliable because it directly impacts the precision of the results generated by the machine learning model. Thus, the continuous quality assurance of the labeled data must be arranged to guarantee the high quality and accuracy of metadata and optimize it when necessary. This stage is often overlooked yet paramount to successful machine learning model training. To implement data quality assurance, annotators commonly use the Consensus algorithm and the Cronbach’s alpha test for continuous QA checks.
ML Model Training & Testing
This is the final stage of the data labelling process, during which the machine learning model is tested on an unlabeled dataset to see whether the estimations or predictions are in accord with the model developer’s expectations. A model must achieve an accuracy of 96% or higher to be considered successfully trained, which is the objective of any AI training model.
Summarizing Data Labeling for Machine Learning
Reliable data labeling is the foundation of the development of any successful computer vision and natural language processing model. The selection of the best AI data labeling approach has a lot to do with your requirements of the data quality for training and your financial and time resources. The data labelling process generally follows a chronological order of data collection, data annotation, data quality assurance and finally, the model training and testing. Data labeling for machine learning algorithms can be conducted using different data tagging tools, whether free online data labeling software like LabelMe, Annotorious, and Sloth or other commercial data labeling tools.
Wordspath can help
Wordspath provides many cost-effective linguistic solutions and support labeling data for machine learning models with guaranteed accuracy and at scale.
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Meanwhile, we are highly experienced in delivering tailor-made localization-related solutions such as desktop publishing, transcription, subtitling, and voiceover. Our ability to quickly handle a wide range of content types between nearly all language combinations sets us apart from our competitors. Should you need to consult on your best-fit language solution, you can contact us through live chat or email to email@example.com. Or simply request a free quote.