How to scale confidential machine learning workloads in isolated cloud environments

How to scale confidential machine learning workloads in isolated cloud environments

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Chutes

Chutes provides serverless AI compute with hardware-backed Trusted Execution Environments, enabling secure, scalable inference for sensitive workloads. It helps developers run confidential models and data in isolated, event-driven environments without managing infrastructure.

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What to expect from an ideal product

  1. Deploy machine learning models in hardware-secured environments that keep your data and algorithms completely isolated from cloud providers and other tenants
  2. Scale inference workloads automatically without provisioning servers or managing clusters, letting you handle varying loads from small tests to production traffic
  3. Process sensitive data through event-driven triggers while maintaining end-to-end encryption, so your information never exists in plain text outside your control
  4. Run confidential AI models where the code, data, and results stay protected even from system administrators and cloud infrastructure operators
  5. Build secure data pipelines that transform messy datasets into clean inputs for ML models without exposing sensitive information during processing

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