Labeled Data

- Overview

Data labeling is an important part of the machine learning (ML) data preprocessing workflow. It involves adding tags or labels to raw data such as images, text files, and videos to specify the context of the model. This enables machine learning models to make accurate predictions.  

Data tags support different ML and deep learning (DL) use cases, including computer vision and natural language processing (NLP). The company integrates software, processes and data annotators to clean, structure and label data. This training data becomes the basis of the ML model.

Data labeling can be manual but is usually performed or assisted by software. High-quality labeling is crucial for industries like insurance or healthcare. In-house data labeling is generally done by data scientists and data engineers hired at the organization. 

Data labeling can be expensive and time-consuming and it can also be prone to human error, which can decrease the quality of the data.

- Labels and Data Labeling

Labeling typically takes an unlabeled set of data and augments each part of it with informative labels. For example, data labels (features, attributes, or categories) might indicate whether a photo contains a horse or a cow, what words were said in a recording, the type of actions performed in a video, what the topic of a news article was, what the overall mood of a tweet was, or an X-ray Whether a spot on the slice is a tumor.

Labels can be obtained by asking humans to make judgments given unlabeled data. Labeled data is much more expensive to acquire than raw unlabeled data.

Data labeling is the process of adding context or meaning to data so that ML algorithms can learn from the labels. It's also known as data annotation or data tagging. 

Data labeling is a preprocessing step in the development of a ML model. It involves: 

• Identifying raw data, such as images, text files, or videos
• Adding one or more labels to specify the context of the data
• Tagging the data with additional information, such as department, location, and creator

- Data Labeling and Training Data

Training data is labeled data that is used to teach an AI model or ML algorithm to make correct decisions.

Data labeling in ML is annotating unlabeled data (such as photos, text files, videos, etc.) and adding one or more insightful labels to provide context to the data so that machine learning models can learn from it. 

The label might say, for example, if the photo shows a bird or a car, which words were said in the recording, or whether the tumor is visible on an X-ray. Data labeling is required for many use cases, such as computer vision, natural language processing, and speech recognition.

Data labeling supports various ML and deep learning (DL) use cases, such as computer vision and natural language processing. For example, if you are trying to build a model for a self-driving car, the training data will include images and videos labeled to recognize cars, street signs, and people. If you're creating a customer service chatbot, data could be all the different ways to ask "what's my account balance?" Text and audio are then translated into different languages.

Training data is critical to the success of any AI model or project. Think of it as garbage in, garbage out. If you train a model on poor quality data, how can you expect it to perform? You can't and won't.

You may have the best fit algorithm, but if you train your machine on bad data, it will learn the wrong lessons, fail to live up to expectations, and not work as you (or your clients) intended. Your success depends almost entirely on your data.