Teradata VantageCloud
Teradata VantageCloud: The complete cloud analytics and data platform for AI.
Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems.
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Ango Hub
Ango Hub is a quality-focused, enterprise-ready data annotation platform for AI teams, available on cloud and on-premise. It supports computer vision, medical imaging, NLP, audio, video, and 3D point cloud annotation, powering use cases from autonomous driving and robotics to healthcare AI.
Built for AI fine-tuning, RLHF, LLM evaluation, and human-in-the-loop workflows, Ango Hub boosts throughput with automation, model-assisted pre-labeling, and customizable QA while maintaining accuracy. Features include centralized instructions, review pipelines, issue tracking, and consensus across up to 30 annotators. With nearly twenty labeling tools—such as rotated bounding boxes, label relations, nested conditional questions, and table-based labeling—it supports both simple and complex projects. It also enables annotation pipelines for chain-of-thought reasoning and next-gen LLM training and enterprise-grade security with HIPAA compliance, SOC 2 certification, and role-based access controls.
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OpenFaceTracker
OpenFaceTracker is a facial recognition program capable to detect one or several faces on a picture or a video, and to identify them via a database. OpenFaceTracker needs OpenCV3.2 and QT4 installed on your machine, you’ve got two options, if you love compiling libraries by hand, please follow build_oft, and installing Opencv and QT using your favorite packaging tool. You can compile OFT as a library or you can compile it as a standalone binary file. You can then open the file and execute the detection and recognition module. You can show help and exit, show the list of all available cameras, you can test the XML DB, read from the OFT config, and check the environment. OpenFaceTrackerLib uses Opencv 3.2. This latter has introduced many new algorithms and features comparing to version 2.4. Some modules have been rewritten, some have been reorganized. Although most of the algorithms from 2.4 are still present, the interfaces can differ.
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