Labelbox, a cloud-based data platform for AI teams, recently raised $110M in a SoftBank-Led Series D. They are also expanding their staff with 20+ new roles.
Labelbox is a platform that helps you get the most out of your data with its comprehensive data lifecycle management and labelling capabilities. This article will discuss how Labelbox helps you get the most out of your data.
What is Labelbox?
Labelbox is an enterprise-grade data labelling platform designed to help you get the most out of your data. With Labelbox, you can securely store and manage large quantities of data while easily creating, training, and deploying AI models faster so your team can instantly benefit from your labelled projects.
Labelbox offers a range of powerful and responsive tools to facilitate rapid annotation workflows, label verification, and active learning algorithms to help teams with annotation efforts. Its user-friendly interface allows for efficient collaboration and scalability so that one person or a team can complete projects quickly without sacrificing accuracy or detail.
Labelbox offers customizable tools for businesses at any stage in the AI development process—from static images to time series datasets like video analytics. Whether it’s image segmentation or text recognition training, Labelbox helps teams structure training data to move quickly into model testing and deployment. Plus, with their cutting-edge active learning capabilities and automated verification toolset, teams can rest assured knowing their models will be trained on quality criteria that meet their exact requirements.
Labelbox Raises $110M in SoftBank-Led Series D
Labelbox, an AI platform for enterprise data annotation, announced it has raised $110 million in a SoftBank-led Series D round. This brings the company’s total funding to just over $180 million since its launch in 2017.
With the new investment, Labelbox will accelerate its mission of simplifying AI enterprise data infrastructure for customers across automotive and life sciences industries. In addition, the company will invest in their data labelling platform, designed to help customers assemble and prepare datasets that power Machine Learning models with greater accuracy and speed. The integration of AI applications is helping companies unlock insights about their products, customers, and supply chains. In 2020 alone, Labelbox helped customers build 500+ models across industries including self-driving cars and bioinformatics research.
Labelbox has seen massive growth since it was founded more than three years ago; they recently secured their spot as a point-solution on the AWS Marketplace making hands-off data labelling even more accessible to businesses of all sizes. With this process becoming smoother, teams can quickly turn significant amounts of inputted data into actionable insights with precision accuracy.
By expanding its customer base and further perfecting its services offering through this new investment round Labelbox is empowering companies around the globe to make sense of more complex datasets while safeguarding invaluable time spent manually organising data platforms with ease and efficiency like never before.
Hiring for 20+ Roles
Organisations of any size need to ensure that their data is properly labelled, accessible, and machine- readable to leverage the immense potential of artificial intelligence. For example, building quality datasets quickly is an essential but expensive process as it requires skilled workers to manage and/or label data.
With Labelbox, you can rest assured that you are leveraging your data more effectively with a robust platform with scalability and collaboration tools.
Labelbox streamlines the process of creating accurate high-quality datasets by assisting organisations in hiring for 20+ roles like Data Annotators, Machine Learning Engineers, Data Scientists, and more. We enable teams to collaborate over large datasets with the help of segmenting them into smaller tasks for each role. In addition, through advanced workflow automation technologies such as active learning or advanced labelling assistants specifically developed for Supervised Machine Learning use cases, Labelbox helps you build reliable efficient training datasets faster than ever.
Labelbox Raises $110M in SoftBank-Led Series D, Hiring for 20+ Roles
Recently, Labelbox raised $110 million in a SoftBank-led Series D funding, which is a testament to the fact that this data annotation and management platform is becoming increasingly popular. Labelbox helps data scientists, engineers, and analysts get the most out of their data, which is why it is a preferred choice for data analysis and management.
Now, let’s explore the different benefits of using Labelbox.
Automates Data Labelling
Labelbox allows users to automate data labelling, reducing the time and cost of manual labelling by allowing users to create automated rules. In addition, the platform automates the creation of training datasets for AI models, enabling labelers to quickly and accurately label data from various sources.
Labelbox also offers automatic workflow configuration, allowing you to customise your preferences for data labelling methods. This helps increase accuracy and delivery speed for labels, resulting in more reliable models with less effort.
Automated workflows save time by ensuring all labels are correctly submitted simultaneously rather than manually entering each label as it’s submitted. As a result, Labelbox labels all data quickly, accurately and consistently across multiple datasets.
Enhances Quality Control
Labelbox enables efficient and accurate quality control for your datasets. With configurable quality control labels and views of consistent annotations, users can reassure themselves that their datasets have high-quality annotations, which can be further examined with Labelbox’s detailed analytics reports.
Labelbox provides automated validation systems and manual validation tools to ensure accurate labelling. Validation tools like adjacent image comparisons or annotation validations allow users to better understand their data edge case scenarios. These manually created rules can be used to flag dataset samples that do not meet predefined requirements or have been labelled incorrectly. Quality Control points within the labelling workflow also provide much more legitimacy to a dataset concerning its quality accuracy.
With an annotated dataset in hand, the next step is to review the data labels from a third-party perspective to verify scale accuracy. With Labelbox’s Data Reviewer feature, you can perform assigned tasks for custom evaluation criteria for cases where crowdsourcing-based solutions alone may not provide sufficient accuracy results or when higher quality levels are necessary on datasets of any tier or size. From operator notes for each sample submitted during the review process returning insightful results through analytics dashboards or even command line access for complex queries against the generated reviews, Labelbox ensures you get the most out of your datasets results by engaging a completely unbiased approach towards manual validation & label reviews.
Streamlines Data Management
Labelbox offers powerful features and options to streamline your data management processes, so you can save time and get the most out of your data. With Labelbox’s streamlined record-keeping tools, you can easily store images, text, audio and documentation in an organised way.
Labelbox also allows for comprehensive labelling operations with different annotation methods tailored to multi-level projects. In addition, Labelbox offers a host of useful reports designed to keep track of the progress of your project, allowing for easier tracking of activities like version control and team collaboration.
Moreover, all your data is stored securely in a cloud-based platform, making it easily accessible from anywhere in the world. This increases productivity and reduces paperwork while ensuring secure storage of confidential data.
How Labelbox Works
Labelbox is a cloud-based platform designed to help organisations and individuals make the most out of their data. Labelbox combines ML annotation and data labelling capabilities to help users create efficient data management pipelines and accelerate the development of their machine learning models.
Labelbox’s strong financial backing – a SoftBank-led Series D round of $110M – and rapid team expansion – recently hiring for 20+ roles – demonstrates the company’s commitment to help organisations better manage their data.
Let’s take a closer look at how Labelbox works.
Create Projects
Labelbox lets you get the most out of your data with its platform for labelling and managing large-scale data sets. To start, users must create a Labelbox project by selecting either a pre-defined project type or adding their custom task.
The pre-defined projects come with default settings such as an image tagging project with labels like “cat” and “dog”, or an object detection project where users can add annotations to images. In addition, users can define their categories and annotations for custom tasks to add to their images.
Once the Labelbox project has been created, users can begin to upload data into a project from their local device, server, cloud storage provider (AWS S3, Google Cloud Platform), or popular datasets like Google Open Image v4 or Kaggle. Labelling can then be performed by assigning humans (via web annotation tool) or models (via machine learning algorithms) to complete the task. After labelling is complete, the labelled data can be organised for export in various formats including but not limited to TFRecord/TFX tensors and .json files.
With this fully integrated platform users save significant time creating training datasets that produce accurate machine learning models.
Label Data
Labelbox helps users to quickly and accurately label data for Machine Learning tasks. The platform allows you to create labels by uploading data, applying the algorithm of your choice, defining the label interface, and automatically onboarding a team of skilled human annotators to perform the task. In addition, Labelbox offers a variety of features and tools to ensure accurate labelling that can be used in multiple industries.
Labelbox makes it easy to create labels by simply importing data into the system. From there, you can define the parameters necessary for developing labels from your dataset, including categories or subcategories that may exist within each item. Furthermore, Labelbox provides an easy-to-use interface for constructing training sets for supervised machine learning models by integrating with various annotation schemes. Finally, Labelbox enables teams of human annotators who can provide high quality labels consistently that are accurate even over longer periods of time.
With its intuitive yet powerful labelling functions, Labelbox is essential to get the most from your machine learning projects and create valuable insights from data-driven decisions more efficiently and accurately than ever.
Review & Validate
Labelbox provides data teams with an easy way to review and validate machine learning predictions. The Review & Validate step of the labelling workflow allows users to confidently vet the results of automated systems, like computer vision models, and adjust them before labelling. This helps ensure that only quality data is used to train machine learning models and reduces the labelling time significantly.
To review & validate predictions, users can pin label specific corrections or comments directly onto a single image or video frame. Taggers can then review colour coded validation pins which are sorted into three categories: errors (red), warnings (yellow), or acceptances (green). This makes it easier for taggers to understand the correctness of their annotations at a glance.
Taggers can also select multiple labels from a side panel to audit prediction accuracy. By selectively displaying only labels associated with one prediction class, reviewers are presented with an array of similar samples where they can begin drilling down into problems and readily identify where further adjustments need to be made.
Using Labelbox’s Review & Validate feature helps data teams quickly assess dataset quality before using for training models and allows them to address issues early on—helping guarantee successful model outcomes downstream.
Labelbox Features
Labelbox is a data annotation platform that helps you get the most out of your data. With Labelbox, you can easily manage larger datasets and collaborate with teammates to create training data quickly and efficiently.
Labelbox was recently funded by SoftBank-Led Series D and is currently hiring for more than 20 roles.
Let’s take a closer look at the features Labelbox offers.
Automated Workflows
By leveraging automated workflows, Labelbox enables teams to speed up the data labelling process, leading to faster and more accurate results. Automated workflows on Labelbox involve machine learning (ML) models to suggest labels, ultimately streamlining the data labelling process. When data is imported into Labelbox, administrators can use an ML model for automatic approval or rejection of data instead of manual approval by a human labourer. With automated workflows enabled, admins can fine-tune the ML models and adjust their accuracy accordingly.
When working with complex tasks, admins can use the “auto-annotate with external model” feature. This feature allows admins to integrate a 3rd-party ML model into the Labelling Interface and run inferences on imported data sets to suggest labels that labels must either accept or reject manually. Additionally, administrators can incorporate automated post-verification into their workflows by creating rules that will reject annotations that don’t meet certain criteria – thus limiting manual edits of laboriously hand-annotated datasets within projects.
Automated workflows are one of many ways that Labelbox helps you get more out of your data while streamlining your labelling workflow simultaneously. By taking advantage of these features, teams can save time and increase accuracy throughout the labelling process.
Data Export & Visualization
Labelbox offers a range of powerful data export and visualisation options to help you visualise, understand, and share your annotated data. Labelbox provides the tools and features to export the data in required formats for further analysis and reporting. In addition, with the ability to select only labelled instances for export, you can easily surface only the annotated elements from your dataset, saving time when building datasets for downstream work.
Data Export: Labelbox enables a wide range of export possibilities such as JSON/CSV/Pascal VOC, which allow you to easily transport your annotations across workspaces. You can even move your annotations without downloading any files through integration with cloud-storage solutions like Dropbox or Google Drive.
Visualisation: You can also choose to generate detailed custom visualisations that show all information related to each annotation including who created the annotation and when it was created or modified. You can even use filters or search terms (like label names) on your visualisations so that they contain only relevant information. With advanced interactive filtering capabilities, it’s easier than ever to quickly sort through your annotations and spot any discrepancies or anomalies in your labelled dataset – ultimately helping ensure higher quality control in massive datasets that would be difficult for one person alone to manage effectively.
AI-Assisted Labelling
Labelbox´s AI-Assisted Labelling technology enables users to quickly and accurately label large amounts of data. With this technology, users can train custom automation models which automatically detect and label objects within an image.
You can significantly increase labelling speed by training automated labelling models such as object detection, OCR text detection, polygon segmentation, etc. Using Labelbox’s AI-assisted labelling tools lowers the time it takes to manually annotate data while maintaining accuracy of annotations.
The AI-assisted models created on Labelbox can then be used to automatically generate labels for new data at a fraction of the time it would take with manual labelling. In addition, the technology is intelligent enough to know when a model needs to be fine-tuned and encourages high accuracy levels with manual annotation options for complex tasks that require greater accuracy than the algorithm provides.
Through supervised automation capabilities and crowd truth algorithms, Labelbox can quickly manage large datasets and ensure high quality labels are generated each time.
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