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aplicações em tempo real

  • Explore scalable machine learning solutions for your enterprise-level data challenges.
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    What is Machine learning at scale?
    Machine Learning at Scale provides solutions for deploying and managing machine learning models in enterprise environments. The platform allows users to handle vast datasets efficiently, transforming them into actionable insights through advanced ML algorithms. This service is key for businesses looking to implement AI-driven solutions that can scale with their growing data requirements. By leveraging this platform, users can perform real-time data processing, enhance predictive analytics, and improve decision-making processes within their organizations.
    Machine learning at scale Core Features
    • Scalable Data Processing
    • Advanced ML Algorithms
    • Real-Time Predictive Analytics
    • Model Training and Deployment
    • Performance Monitoring
  • OAK provides advanced spatial AI capabilities for intelligent perception and interaction.
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    What is OpenCV AI Kit (OAK)?
    The OpenCV AI Kit (OAK) is an innovative platform designed for spatial AI applications. It incorporates advanced features such as real-time object detection, depth sensing, and visual tracking, allowing AI models to better understand and interact with their environments. This hardware-accelerated solution includes a powerful camera system that supports machine learning capabilities, enabling a wide range of applications from robotics to smart surveillance and beyond.
  • An open-source FastAPI starter template leveraging Pydantic and OpenAI to scaffold AI-driven API endpoints with customizable agent configurations.
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    What is Pydantic AI FastAPI Starter?
    This starter project provides a ready-to-use FastAPI application preconfigured for AI agent development. It uses Pydantic for request/response validation, environment-based configuration for OpenAI API keys, and modular endpoint scaffolding. Built-in features include Swagger UI docs, CORS handling, and structured logging, enabling teams to rapidly prototype and deploy AI-driven endpoints without boilerplate overhead. Developers simply define Pydantic models and agent functions to get a production-ready API server.
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