Labelbox is a data annotation platform that efficiently labels datasets for Machine Learning (ML) and Artificial Intelligence (AI) solutions. Using Labelbox, companies can quickly and accurately label images, videos, and other kinds of data using manual annotation, active learning, automation rule-based segmentation. In addition, by leveraging Labelbox’s versatile toolset, businesses can optimise their AI development processes and use cutting-edge technologies to advance their AI solutions.
Labelbox recently introduced new features that make using AI with its platform easier. One noteworthy feature is its integration with Amazon SageMaker GroundTruth, making it possible to use AI in many production jobs. Additionally, Labelbox now offers graphical interface customization so users can tailor their workspace according to certain needs. Furthermore, users can now perform data manipulation tasks without leaving the platform, as Labelbox now provides functionality for filtering datasets and combining different datasets into a single project for annotation. Finally, Labelbox can safely share annotated data within an organisation using user roles or third-party applications such as Slack or Microsoft Teams.
These features highlight how Labelbox is helping businesses accelerate the process of building successful ML/AI solutions by giving them access to powerful tools that enable efficient labelling processes while ensuring accuracy at scale.
Labelbox Overview
Labelbox is a powerful AI platform that helps organisations scale their machine learning (ML) projects. It is a comprehensive AI software that enables data annotation, labelling, and other ML-related tasks.
Recently, Labelbox secured $110 M in Series D led by SoftBank Vision Fund, which further confirms the platform’s potential to help advance AI.
This article will discuss Labelbox and how it could help solve various AI challenges.
What is Labelbox?
Labelbox is an AI training data platform that helps companies manage and improve artificial intelligence models. It enables teams to quickly build datasets and label images, text, audio, and video to accelerate the development of machine learning models.
Labelbox allows users to efficiently collect accurate training data for a wide range of AI applications, including image recognition and segmentation, natural language processing, video understanding, and facial recognition. In addition, the platform’s comprehensive suite of annotation tools make it easy for users to rapidly create datasets tailored to their specific needs.
Labelbox also offers powerful annotation features such as automated labelling processes for large amounts of data; manual annotation tools for drawing, keypoint identification, and text transcription; customizable dataset creation states; a suite of job management components such as user management and analytics showing feature utilisation rates; collaborative environment where team members can share feedback in real time; an integrated version control system that keeps track of revisions made by users throughout the process; an integrated query builder so users can quickly access annotated content by filtering over key properties such as labels or document age range; advanced search options so users can instantly find data based on specific criteria like attributes or concepts within their dataset; support for multiple media formats like CSV/JSON/YAML/XML/Excel spreadsheets among others.
These capabilities allow Labelbox customers to maximise the accuracy of their models while minimising the effort needed to configure them properly by providing high-quality datasets with production-ready labels delivered faster than ever before.
Labelbox’s Impact on AI
Labelbox is a powerful software platform that helps to bridge the gap between data annotation and machine learning. With Labelbox, you can annotate image, text and audio/video data quickly and accurately, making it easier to develop and train AI models. Furthermore, Labelbox streamlines the data annotation process through automated workflows while maintaining quality control at every stage. This helps make AI development faster, cheaper and more efficient than ever.
Labelbox helps to improve accuracy and precision of AI models by providing comprehensive features like supervised learning imports, comprehensive model interoperability support, visual inference tools in the platform – allowing users to identify objects in images with greater speed – NLP-based conversational machine teaching capabilities that allow for greater accuracy when building natural language processing models.
Furthermore, an integrated machine-assisted annotation feature enables computer vision algorithms to annotate images with human-level precision using deep learning technologies such as TensorFlow or PyTorch. Other features such as data transparency visualisations help provide insight into model performance so users can pinpoint areas where improvement is needed. Finally, the flexible architecture of Labelbox makes it adaptable for use in any production environment regardless of size or complexity.
In addition to automating the entire AI development pipeline through its cloud-powered architecture, Labelbox offers an open source frontend SDK, allowing developers to design their custom applications tailored for their specific use cases. As a result of all these advanced capabilities, companies can leverage AI technology for their applications more cost effectively than ever before. With its comprehensive set of tools tailored for model building and innovation management at scale across industries like healthcare education finance media IoT retail & more , Labelbox has become an integral part of businesses seeking success with Artificial Intelligence.
Labelbox Secures $110 M in Series D Led by SoftBank Vision Fund
Labelbox, the leading end-to-end platform that helps organisations accelerate the development of AI and machine learning models, recently announced that it has secured $110 million in its Series D funding round led by SoftBank Vision Fund.
This brings the total amount of money raised by Labelbox to $190.7 million. The funding will be used to expand the platform’s capabilities and further propel the advances of AI, ML, and computer vision for customers in various industries.
Overview of the Investment
Labelbox, a San Francisco-based company that makes software for creating and managing training data for AI models, has recently secured $110 million in Series D funding. This investment was led by SoftBank Vision Fund 2 and joined by existing investors Coatue Management, GV, and Kleiner Perkins.
This funding round will help Labelbox further enhance its features and capabilities as the company expands internationally. With this new injection of capital, Labelbox expects to continue advancing the state of AI with unsupervised learning technologies while reducing time-to-market for the development of high-quality AI applications.
As part of this investment round, Felix Kramer from SoftBank Vision Fund 2 has joined Labelbox’s Board of Directors.
The proceeds from the funding will be used to expand Labelbox’s features set and build out its team globally to better serve customers across different global markets. The investment will also accelerate Labelbox’s core mission: democratise advances in machine learning technology by increasing collaboration between engineers developing machine learning models, domain experts who provide data labelling services, and businesses leveraging new phenomena such as chaos engineering and edge computing.
How the Investment Will Help Advance AI
Labelbox, a leading provider of software and data services for computer vision teams, has just secured an impressive $110M in Series D funding. The round was led by the SoftBank Vision Fund and is the largest known investment in a company aimed at helping businesses leverage artificial intelligence (AI) technology. The fresh capital will accelerate AI training and deployment at scale with enterprise customers and continue fueling Labelbox’s growth as a company.
This investment is exciting not only because it shows significant interest in the technology being developed by Labelbox, but also because it highlights how businesses are beginning to understand that AI can play an essential role in creating solutions that solve actual problems. At its essence, AI has transformative capabilities and this investment can help propel opportunities in the industry through innovation and exploration of new possibilities.
Labelbox’s cloud-based platform helps companies build better computer vision models to solve real-world problems through their teams’ rapid experimentation, prototyping, and analysis process. In addition, this investment gives Labelbox the resources required to expand their platform’s functionality. Finally, it introduces opportunities for strategic partnerships that could open up entirely new applications for computer vision solutions. With these added qualities, we can see an acceleration in the development of AI-driven technologies being applied across industries that have both near and long-term implications for our lives.
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