Data Labeling:
The Authoritative Guide
The success of your ML models is dependent on data and label quality. This is the guide you need to ensure you get the highest quality labels possible.
Contents
Data Labeling for Machine Learning
Machine learning has revolutionized our approach to solving problems in computer vision and natural language processing. Powered by enormous amounts of data, machine learning algorithms are incredibly good at learning and detecting patterns in data and making useful predictions, all without being explicitly programmed to do so.
Trained on large amounts of image data, computer models can predict objects with very high accuracy. They can recognize faces, cars, and fruit, all without requiring a human to write software programs explicitly dictating how to identify them.
Similarly, natural language processing models power modern voice assistants and chatbots we interact with daily. Trained on enormous amounts of audio and text data, these models can recognize speech, understand the context of written content, and translate between different languages.
Instead of engineers attempting to hand-code these capabilities into software, machine learning engineers program these models with a large amount of relevant, clean data. Data needs to be labeled to help models make these valuable predictions. Data labeling is one of machine learning's most critical and overlooked activities.
This guide aims to provide a comprehensive reference for data labeling and to share practical best practices derived from Scale's extensive experience in addressing the most significant problems in data labeling.
What is Data Labeling?
Data labeling is the activity of assigning context or meaning to data so that machine learning algorithms can learn from the labels to achieve the desired result.
To better understand data labeling, we will first review the types of machine learning and the different types of data to be labeled. Machine learning has three broad categories: supervised, unsupervised, and reinforcement learning. We will go into more detail about each type of machine learning in Why is Data Annotation Important?
Supervised machine learning algorithms leverage large amounts of labeled data to "train" neural networks or models to recognize patterns in the data that are useful for a given application. Data labelers define ground truth annotations to data, and machine learning engineers feed that data into a machine learning algorithm. For example, data labelers will label all cars in a given scene for an autonomous vehicle object recognition model. The machine learning model will then learn to identify patterns across the labeled dataset. These models then make predictions on never before seen data.
Types of Data
Structured vs. Unstructured Data
Structured data is highly organized, such as information in a relational database (RDBMS) or spreadsheet. Customer information, phone numbers, social security numbers, revenue, serial numbers, and product descriptions are structured data.
Unstructured data is data that is not structured via predefined schemas and includes things like images, videos, LiDAR, Radar, some text data, and audio data.
Images
Camera sensors output data initially in raw format and then converted to .png or preferably .jpg files, which are compressed and take up less storage than .png, which is a serious consideration when dealing with the large amounts of data needed to train machine learning models. Image data is also scraped from the internet or collected by 3rd party services. Image data powers many applications, from face recognition to manufacturing defect detection to diagnostic imaging.
Videos
Video data also come from camera sensors in raw format and consist of a series of frames stored as .mp4, .mov, or other video file formats. MP4 is a standard in machine learning applications due to its smaller file size, similar to .jpg for image data. Video data enables applications like autonomous vehicles and fitness apps.
Task Audit
Why is Data Annotation Important?
"Garbage in, garbage out." — The fundamental axiom of Machine Learning.
Data is the fuel for modern AI. Without high-quality labeled data, even the most sophisticated algorithms will fail to perform. To ensure your models are robust, accurate, and unbiased, you must prioritize data quality from the very beginning of your ML pipeline.
Accurate annotations provide the ground truth that models compare their predictions against during training. Errors in this ground truth propagate directly into model errors, often magnified. In safety-critical applications like autonomous driving or medical diagnosis, the margin for error is effectively zero, necessitating rigorous quality assurance.
How to Annotate Data
In-House Teams
Highest quality control, but expensive and hard to scale. Best for sensitive data.
Crowdsourcing
Scalable and cost-effective, but requires robust QA mechanisms to filter noise.
Choosing the right annotation workforce depends on your data volume, complexity, and security requirements. Modern approaches also utilize Synthetic Data—data generated programmatically—to bootstrap models in data-scarce environments.
High-Quality Data Annotations
Achieving high-quality annotations is an iterative process. It involves creating a "Gold Set" of perfectly labeled data to benchmark annotators against.
- Develop clear, unambiguous labeling instructions
- Implement consensus voting (multiple annotators per item)
- Regularly audit annotations against the Gold Set
- Provide continuous feedback to annotation teams
Data Labeling for Computer Vision
Computer vision tasks rely on precise spatial annotations. From 2D bounding boxes for object detection to pixel-perfect semantic segmentation for scene understanding, the granularity of labeling dictates the model's capabilities.
NLP Data Labeling
In Natural Language Processing, context is king. Labeling involves Named Entity Recognition (NER) to identify people, places, and organizations, as well as Sentiment Analysis to gauge emotional tone.
Modern LLMs require even more complex annotation, such as "Reinforcement Learning from Human Feedback" (RLHF), where humans rank model outputs to align them with human, helpful, and harmless intent.
Conclusion
Data labeling is not just a utility; it is a strategic asset. By investing in high-quality data pipelines, you build a moat around your AI products. As models become commoditized, your proprietary, high-quality labeled data becomes your most valuable differentiator.
Questions people ask
What are these how-to guides?
Step-by-step builds for things teams actually ask us to do — connecting an agent to Slack and Google Docs, building a RAG agent over internal knowledge, building a voice agent for lead qualification. Each one is a working build, not an overview.
Are they vendor-neutral?
The architecture is. Where we use a specific tool we say why and what the alternatives are, so you can substitute what you already run.
What level do they assume?
Comfortable with APIs and a backend language. No ML background required — none of these need you to train anything.
How long does each build take?
A working version in an afternoon; production-hardening is the longer part and each guide is explicit about what it skipped.
Will these work in production as written?
They get you a correct prototype. Each guide ends with what is missing for production — evaluation, fallback handling, permissions — because shipping without those is where teams get hurt.
Can we use this code commercially?
Yes. The snippets are there to be used. Attribution is appreciated but not required for code.
What if we get stuck?
Book a working session. If you are stuck on a step in one of these it usually takes minutes on a call to unblock.
Do you keep them updated?
Yes — APIs and model behaviour move, and a guide that no longer runs is worse than no guide. Where something has changed recently the guide says so.
Can you build this for us instead?
That is the day job. The guides exist because plenty of teams would rather build it themselves, and that is a legitimate choice.
What should we read first?
The RAG guide if you have a knowledge base, the hallucinations guide if answers invent facts, the function-calling guide if the agent needs tools. Each is self-contained.