Today, tackling technological challenges feels a lot like what Andrew McAfee, Co-director of the MIT initiative, said: “The world is one big data problem.” Whether we’re debating our origins or diving into the heart of AI, one thing is clear — data is at the core of everything.
Meanwhile, data labeling for machine learning became pivotal for streamlining the way we handle this massive data puzzle. Yet, most companies still struggle to get this process done right for their ML projects. Those that do know how to label data for machine learning are already in the pace for speeding up the development of intelligent and dependable AI systems.
Our expert team shares some key insights and tips on the process of data labeling in machine learning.
Where to Start with Data Labeling for Machine Learning Projects?
Data labeling is crucial for machine learning as it is the only way a machine can understand the data, learn from it, and perform a given task (e.g., prediction, recommendation, recognition).
Most ML models use supervised learning, where an algorithm maps inputs to outputs based on a set of labeled data. This involves humans labeling raw, unlabeled data, such as tagging a set of images for an autonomous system with information about obstacles on the road. The model learns from these labeled examples to decipher patterns in that data during a process called model training. The resulting trained model can then make predictions on new data.
The accurately labeled data in machine learning used for training and assessing a model is often referred to as “ground truth.” The model’s accuracy relies on the precision of this ground truth, emphasizing the importance of investing time and resources in accurate data labeling.
The Essential Ingredients of Labeling Data for Machine Learning
Given one of the key trends this year, which is data-centric AI, data labeling has become a pivotal aspect in this approach. And so the data labeling standards are quite high. If you think of handling this task on your own or, perhaps, building an internal labeling team, there are some core pillars to consider.
The first and the most crucial ingredient in the complex process of labeling data for ML is, of course, data. This covers all the data-related steps, including data collection in machine learning, data cleaning, and the actual data labeling. The next key factor to think about is that data labeling for machine learning cannot do without human supervision. Human annotators provide the essential context, expertise, and reasoning to enhance the automated process.
Besides, a manual approach may not always work best for edge cases or when you have large datasets. The best way to speed up the process is to combine human intelligence with machine learning models for automated annotation. This technique is known as human-in-the-loop (HITL). There’s also active learning, wherein an ML algorithm engages interactively with a user to strategically select and query for labels on specific examples from an unlabeled dataset.
“Just as NLP requires more brainpower to process textual information and delve into the core of the text, computer vision also demands a keen eye from data annotators. They must pay attention to the smallest details in each pixel being labeled.”
Viktoriia Yarmolchuk
Account Manager at Label Your Data
How to Label Data for Machine Learning in 10 Steps: Label Your Data Best Practices
Our expert team shares the key steps to help you better understand how to label data for machine learning on your own. It’s a sequence of clearly defined steps, with each playing a role in the production of a high-quality dataset:
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Choosing a labeling approach
As you start planning out your ML project, the first thing to do is to decide on the approach you’ll take to get training data. There are three options to choose from:
- Build your in-house team
- Outsource to experts
- Use a data labeling platform
Your choice would depend on the complexity of your project, the volume of training data, the size of the data labeling manpower required, as well as the financial and time resources you can dedicate to this ML project. You can learn more about this in our article about in-house vs. outsourced data annotation.
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Measuring project scope
Calculate the size of the dataset you need to annotate. Additionally, consider the complexity of the annotation task and the required level of expertise, as these factors can impact the overall time and resources required for the project.
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Defining data labeling criteria
You need to figure out what your project is all about. Prepare clear instructions by examining the structure of a machine learning project, what the ML model has to predict or classify, and what types of data labeling and label categories (i.e., “dog,” “vehicle,” “pedestrian”) are needed.
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Collecting and preparing data
Data collection implies gathering raw, unstructured data that needs to be labeled. You may need images, videos, text documents, or audio files for your specific project. The next step is preparing data for supervised machine learning by cleaning it. That is, eliminating irrelevant, duplicate, or corrupted files to uphold data quality. This keeps your model results accurate.
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Identifying edge cases
Examine your dataset and identify any unusual instances where the annotations lack clarification according to the provided instructions. Compile a list of these unique scenarios, or at the very least, identify the typical ones.
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Selecting the tool
Choose the appropriate tooling based on annotation type and desired output. This can range from simple software to advanced AI-assisted platforms for data labeling.
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Annotators training
Usually, you need to train a team of annotators to use a specific labeling tool and follow the project guidelines. You can skip this step if you’re planning to handle the process completely on your own. But if not, or when dealing with a specific type of data (e.g., medical or finance data), you’ll need to hire people with the appropriate knowledge and domain-specific expertise, and train them.
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Labeling data
Now to the best part. In AI, understanding what is labeled training data involves recognizing the significance of a well-organized process of data labeling for effective model training. This implies integrating all the above steps into your workflow to label data in machine learning according to the determined requirements.
At Label Your Data, this process is often semi-automated. Yet, each labeled data point is meticulously reviewed by human experts through a thorough QA.
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Quality assurance (QA)
Regular QA procedures are crucial to verify label accuracy and consistency. This includes reviewing random data samples or employing validation techniques. The labeling process also follows an iterative loop, with initial results reviewed and feedback incorporated for further label refinement.
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Deploying the labeled dataset
Once you’ve labeled data in machine learning and checked the quality and consistency of the performed annotations, it’s time to put the labeled dataset to use. More specifically, the dataset can now be split into training and testing data in machine learning for model training, testing, and validation, respectively.
Labeling data for ML training is an iterative process, involving constant monitoring, feedback, optimization, and testing. If you’re dealing with a large dataset or a very specific use case and seek professional help, contact Label Your Data!
How to Label a Dataset for Machine Learning in Computer Vision
The process of labeling data for machine learning usually falls under two categories: machine vision tasks and NLP tasks. If you’re building a computer vision system, you deal with visual data, such as images and videos. Here, you can use more than one type of data labeling tasks to generate a training dataset, including:
Image Categorization: Allows training your ML model to group images into classes, allowing further identification of objects in photos.
Semantic Segmentation: Associates each pixel with an object class, creating a map for machine learning to recognize separate objects in an image.
2D Boxes (Bounding Boxes): This labeling type implies drawing frames around objects for a model to classify them into predefined categories.
3D Cuboids: Extends 2D boxes by adding a third dimension, providing size, position, rotation, and movement prediction for objects.
Polygonal Annotation: Draws complex outlines around objects, training machines to recognize objects based on their shape.
Keypoint Annotation: Defines main points for natural objects, training ML algorithms for shapes, movement, and versatility in facial recognition, sports tracking, etc.
Object Tracking: This type is often used in video labeling, breaking down frames, and detecting objects to link their positions across different frames.
As such, when labeling data for ML training, keep in mind the specific task you want your ML model to perform. You can use your labeled dataset to build a computer vision model for identifying facial expressions, recognizing handwritten text, segmenting medical images, or even predicting anomalies in satellite imagery. Everything your heart desires.
How to Label Data for Machine Learning in NLP
Labeling data for machine learning can get trickier when working with textual or audio data. The reason for this is simple: inherent subjectivity and complexity in language. The key challenge here is the need for linguistic knowledge.
Creating a training dataset for natural language processing (NLP) involves manually annotating key text segments or applying specific labels. This includes tasks such as determining sentiment, identifying parts of speech, categorizing proper nouns like locations and individuals, and recognizing text within images, PDFs, or other documents.
The main NLP labeling tasks include:
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Text Classification: Groups texts based on content using key phrases and words as tags (e.g., automatic email filters categorizing messages).
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OCR (Optical Character Recognition): Converts images of text (typed or handwritten) into machine-readable text, applied in business, license plate scanning, and language translation
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NER (Named Entity Recognition): Detects and categorizes specific words or phrases in text, automating the extraction of information like names, places, dates, prices, and order numbers.
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Intent/Sentiment Analysis: Combines sentiment analysis (classifying tone as positive, neutral, or negative) and intent analysis (identifying hidden intentions) for applications in market research, public opinion monitoring, brand reputation, and customer reviews.
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Audio-To-Text Transcription: Teaches ML model to transform audio into text, useful for transcribing messages and integrating with other NLP tasks like intent and sentiment analysis for voice recognition in virtual assistants.
“NLP can be challenging as it involves reading texts and thinking critically about the content while labeling data. Additionally, when working with texts in a foreign language, accurate translation becomes crucial to execute the task correctly. This is why, in some cases, NLP may demand more resources to achieve success in your ML project.”
Ivan Lebediev
Integration Specialist at Label Your Data
Let’s Sum Up
Effective ML models rely on extensive amounts of top-notch training data. Data labeling for machine learning allows us to get this training data for developing smart solutions in either computer vision or NLP domain. However, annotating data for ML models is frequently a costly, intricate, and time-intensive undertaking.
With our guide, you get all the necessary information about how to label a dataset for machine learning, and even take on the challenge and try to label a dataset on your own.
If any issues arise, consider streamlining your machine learning project by leveraging our professional data labeling services. Send your data to us and get a free pilot to see how it works!
FAQ
Does machine learning always need labeled data?
Not necessarily. Machines can leverage both labeled and unlabeled data for model training purposes. However, while labeled data is commonly used in supervised learning, machine learning techniques such as unsupervised and reinforcement learning can operate without labeled data.
What type of data is best for machine learning?
Data can take different forms, but machine learning mainly uses four types: numerical data, categorical data, time series data, and text data. For NLP projects, you need text or audio data, while for computer vision you use visual data, such as images and videos. The best type of data, though, depends on the specific task, but generally, you need well-organized, representative, and diverse datasets.
Which type of machine learning uses labeled data?
Supervised learning is a type of machine learning that uses labeled data to train algorithms, enabling them to generalize patterns and make predictions on new, unseen data.
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