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Top 10 Multisensory Data Training Platforms in 2023


Contents:


 

In data-centric AI, the focus is on using large amounts of data to train machine learning models and improve their performance on a given task.


Multi-sensory data refers to data that is collected from multiple senses, such as visual, audio, text, and other types of data. Training machine learning models on multi-sensory data can be challenging because these data types often have different characteristics and require different types of processing and analysis. Using multi-sensory data is a good way to train models that can improve performance and make them more robust, meaning they are less sensitive to variations in data and can handle a wider range of input conditions.

In Real-world, the most popular applications include autonomous vehicles, virtual assistants, and healthcare systems, involving the integration of multiple sensory inputs. Using a multi-sensory data training platform can help to simplify the process of building and deploying these systems.


Surround sensors used for environment perception in autonomous vehicles*



Which platform has the best performance on data annotation, visualization, and curation?

Here are the answers:


#1 BasicAI


BasicAI Cloud Platform is a data-centric MLOps platform that efficiently develops and iterates your AI model. Your entire AI lifecycle is taken care of with reproducibility, manageability, and automation.




· Multisensory data support



· Key Features of MLOps Data


Empower your machine learning with accurate and reliable dataset from BasicAI Platform.

BasicAI Cloud boasts a powerful annotation suite supporting multisensory data. Apart from Image Annotation, Audio, Text and 3D Point Cloud Annotation, the data labeling tool's main focus is on 2D & 3D Fusion datasets, including Lidar-camera Fusion and 4D Radar.

Data Curation: After data annotation or once the model has been trained, it will typically need to be curated to ensure that it is performing accurately and effectively. This involves Data integration, Data versioning, Data organization, Data validation etc.

Ontology Center: It applies the data requirements to the data annotation interface of the entity, class and classification configuration. Futuremove, the ontology center in BasicAI is focused on developing and maintaining ontologies related to AI and machine learning.



· Software and Price


With open source software, AI/ML engineers can experience 0 cost, and transparency on how it works. Meanwhile, it is flexible and customizable, which means that users can modify and tailor the software to meet their specific needs with users' data and software.

On the other hand, some companies in special industries have higher requirements for data control and security and for compliance and available resources reasons, the on-premise version software is a must.


Xtreme1 project is now hosted by LF AI & Data Foundation as a sandbox project and maintained by BasicAI team.



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Scale AI is a company that provides data annotation and machine learning infrastructure services. The company was founded in 2016 and is headquartered in San Francisco, California. Scale AI offers a range of services to help organizations build and improve their machine learning systems, including data annotation, data management, and machine learning infrastructure.



· Multisensory data support



· Key Features of MLOps Data


Scale Rapid supports general image annotation, 2D semantic segmentation, text collection, document transcription, named entity recognition, and video playback annotation. Text collection is a general task type where Scale can gather information from given attachments or web links. Example tasks could include creating a description from an attached image or querying website information given a link.



The company's data annotation services include image and video annotation, text annotation, and 3D annotation, and they are designed to help organizations prepare data for machine learning and improve the accuracy of their machine learning models.


· Software and Price



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Amazon SageMaker Ground Truth is a machine learning service provided by Amazon Web Services (AWS) that makes it easier to build high-quality training datasets for machine learning models. The service helps to quickly and easily label and annotate data, which is a time-consuming and costly task when done manually.



· Multisensory data support



SageMaker Ground Truth provides several built-in capabilities, such as automatic annotation and annotation consolidation, to help reduce the amount of manual labeling required. The service also supports custom workflows, so you can use your own annotation tools or work with a crowd sourcing vendor of your choice. Additionally, SageMaker Ground Truth includes built-in support for several common data annotation tasks, such as image and video classification, object detection, and semantic segmentation.


· Key Features of MLOps Data



· Software and Price



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Appen provides data annotation services for machine learning and artificial intelligence projects. They specialize in providing high-quality, human-annotated data that is used to train, test and validate machine learning models.



· Multisensory data support



· Key Features of MLOps Data



Appen's data annotation platform is cloud-based, which allows customers to access their data and projects from anywhere, and to collaborate with Appen's teams of annotators. The platform includes tools for quality assurance, data management, and reporting.

Appen has a global network of over 1 million professional annotators that allows them to handle a large volume of data, in multiple languages and for different projects types and complexity.


· Software and Price



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V7 offers a complete toolkit for your training data engine: Automated labeling tools, models in the loop, annotation services, and a powerful API. V7’s focus today is on computer vision and automatically identifying and categorizing objects and other data to speed up how AI models are trained. V7 says it needs just 100 human-annotated examples to learn what it needs to do.



· Multisensory data support




· Key Features of MLOps Data


V7’s specific USP is automation. It estimates that around 80% of an engineering team’s time is spent on managing training data: labeling, identifying when something is incorrectly labeled, rethinking categorizations and so on, and so it has built a model to automate that process.



· Software and Price



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SuperAnnotate's Annotation editor is supercharged with advanced and user-friendly tools.



Pixel-accurate annotations: Divide your images into multiple segments with the smart segmentation tool to create clear-cut annotations in no time.


· Multisensory data support



· Key Features of MLOps Data


Pixel Editor - Semantic and instance segmentation: Switch between semantic and instance segmentation modes depending on your project requirements.

Vector Editor - Template: Create templates or use one of the available ones to annotate faster and with higher Powerful vector editor packed with advanced tools to annotate images and videos with high accuracy.



Effective team communication: Comment on an image to open a chat window and communicate about annotation mistakes and requirements with your team.


· Software and Price



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Labelbox was founded in early 2018 to empower organizations building the Al solutions that will drive the next generation of products and services. Our founders experienced first hand the pain of developing and maintaining in-house tools — as well as the benefits of reliable, well- designed MLOps solutions.



Labelbox natively supports image, video, text, PDF document, tiled geospatial, medical imagery, and audio data.


· Multisensory data support



· Key Features of MLOps Data


Catalog - Surface and prioritize the most important data for labeling.

Annotate - Access a full suite of labeling, collaboration, and quality tools across a variety of data types.

Model - Test and evaluate models and mine rare examples to boost model performance.