Deployo.ai

Deployo is an AI infrastructure platform designed to simplify model deployment and productionization. It offers cloud-agnostic deployment, scalable and secure infrastructure, and seamless integration with existing ML workflows so teams can bring models to production quickly without heavy DevOps overhead.

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Overview

Deployo enables teams to deploy machine learning models as live, scalable APIs with automatic resource allocation and GPU-optimized performance. It supports diverse deployment targets (public cloud, on-prem, VM-friendly environments) and aims to remove vendor lock-in while providing enterprise-ready capabilities.

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Key Capabilities

• Cloud-agnostic deployment: deploy anywhere, without hard dependencies or vendor lock-in.
• Model-to-prod in minutes: go from training to production rapidly, without complex deployment scripts.
• GPU-optimized performance: efficient inference and resource utilization.
• Automatic resource allocation: scales compute up or down based on usage; supports load balancing and request batching.
• Serverless and scalable options: deployment modes that fit varying workloads.
• Swagger endpoint documentation: automatic API docs for deployed models.
• Automatic network policy management and deployment strategies: secure, controlled exposure of APIs.
• Multi-model support: integrates with multiple models and chaining; supports custom Python packages.
• Integrations: weights & biases and HuggingFace integration for streamlined workflow.

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How It Works

1. Bring your ML model (any framework).
2. Deploy directly from your existing tooling (Weights & Biases, HuggingFace, etc.).
3. Deployo provisions a live API with scalable, secure endpoints and auto-scaling.

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Safety and Security Considerations

• Enterprise-ready with secure deployment practices, policies, and scalable infrastructure. Ensure compliant handling of data according to organizational guidelines.

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Core Features

• Cloud-agnostic deployment and no vendor lock-in
• Quick path from training to production (minutes, not weeks)
• GPU-optimized inference and scalable API hosting
• Automatic and horizontal/vertical autoscaling
• Request batching and load-aware distribution
• Serverless deployment options
• Swagger endpoint documentation for deployed APIs
• Automatic network policy management and deployment strategies
• Multi-model support with chaining and custom Python packages
• Integrations with Weights & Biases and HuggingFace
• On-prem and VM-friendly deployment options