Data Labeling for ML: Approaches and Tools

- Overview

In machine learning (ML), data labeling is the process of identifying raw data and adding labels to provide context. The labels help ML models learn from the data and make accurate predictions.

Data labeling tasks can include: Data tagging, Annotation, Classification, Moderation, Transcription, Processing.Data labeling can involve identifying objects in raw data, such as: Images, Text files, Videos, Audio. 

Labels represent the true outcome of the target, which is the final output an ML model is trying to predict. Labels are assigned to the training dataset, but when the ML model is ready, it is fed with unlabeled data.

Some best practices for data labeling include:

• Standardizing the tags
• Using all applicable tags
• Not over-tagging
• Re-evaluating tags over time

- Data Labeling

The quality of a ML project comes down to how you handle three important factors: data collection, data preprocessing and data labeling.

Data labeling (or data annotation) is the process of adding target attributes to training data and labeling them so that a machine learning model can learn the predictions it expects to make. This process is one of the stages of preparing data for supervised machine learning.

For example, if your model had to predict whether a customer review was positive or negative, the model would be trained on a dataset containing different reviews labeled as expressing positive or negative sentiment.

In many cases, data labeling tasks require human interaction to assist machines. When experts (data annotators and data scientists) prepare the most suitable dataset for a project and then train and fine-tune the AI model, this is called a Human-in-the-Loop model.

- How are Data Labels Implemented?

To clean, organize, and label data, companies integrate software, programs, and data annotators. These labels allow analysts to isolate certain variables in the dataset, facilitating selection of the best data predictors for ML models. Labels specify which data vectors should be used for model training, during which time the model improves its ability to predict the future. Machine learning models are built on top of this training data.

Data labeling requires Human-in-the-Loop (HITL) participation and machine support. HITL uses the expertise of human "data labelers" to train, test, and improve machine learning models. They help guide the data labeling process by providing the model with the dataset most relevant to a particular project.

Here are some steps you can take to label a dataset:

• Define the type of data you need for training
• Define the characteristics of the labeled data your model needs
• Decide how much labeled data of each type you need
• Choose a way to label the training data
• Break down the labeling task
• Write clear instructions

You can also streamline data labeling by using semi-supervised learning, which is a training style that uses both labeled and unlabeled data. For example, you can label a part of a dataset to train a classification model. 

Some recommend using a blended approach, which uses both automated and external data labeling. External labeling may pose some data security risks, but in most cases, the data being labeled is not sensitive.

- Data Labeling Approaches for ML

Data labeling can be performed in a number of different ways. The choice of approach depends on the complexity of the problem and training data, the size of the data science team, and the financial and time resources the company can allocate to implementing the project.

Data labeling for ML can be roughly divided into five categories.

• Internal: As the name suggests, this is when your data label is your own team of data scientists. There are many immediate benefits to this approach: easy tracking of progress, and reliable levels of accuracy and quality. However, outside of large companies with in-house data science teams, in-house data labeling may not be a viable option.
• Outsourcing: Outsourcing is a great option to create a team to tag a project over a period of time. By promoting your program through a career site or your company's social media channels, you can create a channel for potential applicants. From there, an interview and testing process will ensure that only those with the right skills make it onto your tag team. This is a great way to set up a temporary team, but it also requires some planning and organization; your new hires will need to be trained to familiarize themselves with their new jobs and do what you ask them to do. Also, if you haven't already, you may also need to license the data labeling tools for your team to use.
• Crowdsourcing: A crowdsourcing platform is a way of getting help from people around the globe to complete a specific task. Because crowdsourced work can be picked up from anywhere in the world and executed as tasks become available, it is very fast and cost-effective. However, crowdsourcing platforms can vary widely in terms of workforce quality, quality assurance, and project and workforce management tools. So when looking at crowdsourcing options, it's important to understand how platforms handle these factors.
• Composition: A composition tag is the creation or generation of new data that contains the desired properties of an item. One way to perform synthetic labeling is through generative adversarial networks (GANs). GAN utilizes two neural networks (generator and discriminator) to compete to create fake data and distinguish real data from fake data respectively. This produces highly realistic new data. GANs and other synthetic labeling methods allow you to create entirely new data from pre-existing datasets. This makes them time-efficient and excellent at generating high-quality data. Currently, however, synthetic labeling methods require a lot of computing power, making them very expensive.
• Programmatic: Programmatic data labeling is the process of automatically labeling data using scripts. This process automates tasks including image and text annotation, eliminating the need for large numbers of human annotators. Computer programs also don't need to rest, so you can get results faster than working with humans. However, these processes are far from perfect. For this reason, programmatic data labeling is often used in conjunction with a dedicated quality assurance team. The team reviews datasets as they are labeled.

Each different data labeling method has its advantages and disadvantages. Knowing which method is best for you depends on a variety of factors. These might include the complexity of the use case, training data, size of the company and data science team, financials, and deadlines. It is important to keep these in mind when considering data labeling solutions.

- Data Labeling Tools for ML

• Amazon SageMaker Ground Truth: Amazon offers a cutting-edge autonomous data labeling solution called Amazon SageMaker Ground Truth. The solution simplifies datasets for machine learning by providing a fully managed data labeling service. 
• Heartex: Heartex provides data labeling and annotation tools for building accurate and intelligent AI products. Heartex's tools help companies minimize the time teams spend preparing, analyzing, and labeling machine learning datasets. 
• Sloth: Sloth is an open source program for data labeling, created primarily for computer vision research using image and video data. It provides dynamic tools for computer vision data labeling. 
• Playment: With ML assistive tools and advanced project management software, Playment's versatile data labeling platform provides a secure, personalized workflow for creating high-quality training datasets. 
• LightTag: LightTag is an additional text tagging program for generating specific datasets for NLP. The technology is set up to work with ML teams in a collaborative workflow. It provides a greatly simplified user interface (UI) experience to manage workforce and facilitate annotation. In addition, the program provides first-class quality control tools for precise labeling and efficient dataset preparation. 
• Amazon Mechanical Turk: Amazon Mechanical Turk, also known as MTurk, is a well-known marketplace for crowdsourcing services, often used for data labeling. As a requester on Amazon Mechanical Turk, you can create, publish, and manage various human intelligence activities (commonly referred to as HITs), such as text classification, transcription, or surveys. The MTurk platform provides useful tools for describing your tasks, choosing consensus criteria, and specifying the amount you are prepared to pay for each project. 
• Computer Vision Annotation Tool (CVAT): Digital images and movies can be annotated using the Computer Vision Annotation Tool (CVAT). CVAT offers extensive functionality for labeling data for computer vision, although the program takes some time to learn and master. The program supports tasks such as object detection, image segmentation, and image classification. 
• V7: The most powerful computer vision training data platform is V7. V7 is an automatic labeling platform that combines dataset management, image and video labeling, and autoML model training to perform labeling tasks. 
• Labelbox: Labelbox provides the right annotation solution for any activity, giving you complete visibility and control over every aspect of your labeling process.
• Doccano: Doccano, an open source annotation tool for machine learning practitioners. It provides job annotation capabilities, including sequence labeling, sequence-to-sequence, and text classification. For sentiment analysis, named entity recognition, text summarization, and more, Doccano allows you to create labeled data. A data set can be completed in a few hours. It features collaborative annotations, support for multiple languages, smartphone compatibility, emoji compatibility, and a RESTful API.
• Supervisely: Supervisely is a powerful computer vision development platform that enables independent researchers and large teams to experiment with and annotate datasets and neural networks. It can be used with GPU and CPU. Modern class-neutral neural networks for object tracking are built into video labeling tools. It also has a REST API that allows integration of custom tracking NNs. There are also OpenCV tracking, linear and cubic interpolators.
• Common Data Tools: Common Data Tools provide tools and standards for creating, collaborating, labeling, and formatting datasets, enabling anyone without a data science or engineering background to work on the next wave of powerful, practical, and important artificial intelligence application. Common data tools are user-friendly, accessible, and developer-friendly.
• Audino: The collaborative and cutting-edge open-source tool for speech and audio annotation is called Audino. Annotators can use this tool to define and describe time segments of audio files. Dynamically generated tables make labeling and transcribing these sections simple. Administrators can centrally manage user roles and project assignments through the dashboard. Dashboards also allow label descriptions and value descriptions. For additional processing, annotations can easily be exported in JSON format. Through a key-based API, the tool can upload and distribute audio data to users. The flexibility of the annotation tools allows annotation for a variety of tasks, including speech scoring, voice activity detection (VAD), speaker recognition, speaker characterization, speech recognition, and emotion recognition. Thanks to the MIT open source license, it can be used for professional and academic applications.
• SuperAI: Super.AI is an AI-based data labeling platform that leverages human expertise and AI techniques to generate, organize, and label various forms of data. The platform leverages a new approach to data labeling and machine learning called data programming, executed by its proprietary AI compiler. The platform takes a pipeline-like approach to breaking down complex tasks into smaller, more manageable components that are gradually automated over time.
• SurgeAI: Surge AI is a data labeling platform that uses lightning-fast labelers designed for the complex challenges of NLP. Their platform integrates sophisticated quality controls, breakthrough technologies, and vibrant APIs to provide you with datasets that fuse the richness and nuance of language, and powerful tools to unify the labeling process.
• Encord: Encord is a comprehensive AI-assisted platform for collaboratively annotating data, orchestrating active learning pipelines, fixing dataset errors, and diagnosing model errors and biases.

- Steps To Label A Dataset in Python

Here are some steps you can take to label a dataset in Python: 

• Import the necessary libraries.
• Load the data into a pandas DataFrame.
• Create a new column for the labels.
• Label the data manually or with the help of a tool like Label Studio.
• Save the labeled data to a file.

Here is an example of how to label a dataset in Python using the Label Studio tool:

This is just a simple example, and there are many other ways to label a dataset in Python. The best method for you will depend on the specific needs of your project.