Data Labeling Software – Top 5 Advantages | Data Labeling Services in USA

Data Labeling Software – Top 5 Advantages

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

  1. 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.
  1. 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.
  1. 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
  1. 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
  1. 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.

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