Squire

Machine Learning Model Deployment

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An AI model evaluation and switching management system continuously monitors the performance of different artificial intelligence models in real-world applications to ensure optimal and safe workload handling. Its key features include background model testing, confidence-based decision making, seamless integration with existing apps, real-time metrics analysis, privacy-preserving prompt handling, automated workload comparison, and safeguards to prevent premature switching, making it suitable for developers, data scientists, and organizations seeking cost-effective, reliable, and adaptive AI deployment solutions.

Squire can be found in Machine Learning Model Deployment categories.

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Squire Main Features

• Run History
  Squire saves metrics and history of model runs for review.
• Web Dashboard
  You can create and manage workloads using the online dashboard.
• Workload Keys
  You can create a workload and copy a special key to identify it.
• Prompt Privacy
  Your prompts and outputs are stored briefly and then deleted to keep data private.
• Decision Making
  Squire gives you a recommendation to switch models, test more, or stay with the current one.
• Production Path
  Your app runs normally without changes, keeping your path unchanged.
• Decision Viewing
  You can see the decision through the dashboard or by calling an API.
• Model Evaluation
  Squire compares models in the background and suggests whether to switch or not.
• Background Testing
  Squire runs tests while your app is working, without stopping it.
• Shadow Data Sending
  Your app sends requests normally, and Squire observes the outputs after they are processed.
• Metrics Only by Default
  Squire only collects metrics unless you choose to make changes.
• Automatic Queue Clearing
  Raw data waiting to be tested clears automatically after a while.
• Model Switching Decisions
  Squire tests different AI models in the background and tells you when to switch based on safety.
• Simple Python Integration
  You can add a background request in your code so your app still works if Squire fails.

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