Data Labeling – Overview
- The data labeling process helps in converting raw data into a labeled form for machine learning.
- By doing so, an ML model learns patterns that are repetitive, recognize and implement on future raw data.
- An ML project needs the data so it can “learn.” In this era of AI & Machine Learning technology, to automate the labeling process, data labeling tools play a key role in automating the process, which is particularly tedious.
- Not only that, for the overall dataset creation process, data labeling tools are easier, collaborative, and produce higher quality datasets.
- Organizations use data labeling tools to identify raw data for the ML model – be it text, videos, audio, and any other file format.
- Since all Organization strategies vary, using a template solution will never produce results.
- Hence, open code data labeling platforms are considered as an effective solution in such scenarios.
Data Labeling – Market Size
The global data collection and labeling market size are expected to reach USD 8.22 billion by 2028, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 25.6% from 2021 to 2028.
Data Labeling Software – Top 5 Advantages
- Versatility & Secure
- Unlimited Data sets
- Smart Algorithms
- Multi Framework Support
- Easy Deployment
Data Labeling Software – Top 5 in 2022
- Amazon SageMaker
- Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning (ML) models in the cloud.
- SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.
- It provides several built-in ML algorithms that developers can train on their own data.
- It also provides managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch.
- Dataloop
- Dataloop is an enterprise-grade data platform for vision AI systems in the development and in production
- The Dataloop platform streamlines the process of preparing visual data for machine learning
- It is a one-stop-shop for building and deploying powerful computer vision pipelines – data labeling, automating data ops, customizing production pipelines, and weaving the human-in-the-loop for data validation.
- It eliminates data challenges for companies, allowing them to focus their resources on their core business.
- Appen Figure Eight
- Appen Figure Eight is a human-in-the-loop machine learning and artificial intelligence company based in San Francisco
- Figure Eight technology uses human intelligence to do simple tasks such as transcribing text or annotating images to train machine learning algorithms
- It automates tasks for machine learning algorithms, which can be used to improve catalog search results, approve photos, or support customers
- This technology can be used in the development of self-driving cars, intelligent personal assistants, and other technology that uses machine learning
- SuperAnnotate
- It is an end-to-end platform to annotate, version, and manage ground truth data for your AI
- It can automate and scale your AI pipeline 3-5x faster with the most powerful toolset, robust data management system, and industry-leading annotation services
- It can annotate an image, video, and text with faster data throughput
- It offers comprehensive multi-level quality management and effective collaboration tools to drive successful projects and boost model performance
- Darwin V7
- V7 is one of the leading platforms for a new breed of software ushered by deep learning
- It is used to collaborate and automate workflows, so you can reach human accuracy faster with 10x more training data
- It automates labeling, enables unparalleled control of your annotation workflow, helps you spot quality issues in your data, and integrates seamlessly into your pipeline
- It is built in Elixir, an Erlang-based language to handle massive scale concurrency between millions of users moving billions of images.
