Protopia AI's Stained Glass Transform (SGT) is a groundbreaking solution for organizations looking to leverage sensitive data in AI applications with robust security and flexibility. SGT transforms data into unintelligible forms, safeguarding against breaches and minimizing exposure risks without sacrificing fidelity. By enabling full data richness, SGT boosts value derived from AI models.
With its low compute overhead, SGT operates efficiently on commodity hardware, freeing up powerful GPUs for critical tasks and offering greater flexibility across on-prem, multi-tenant, and edge environments. SGT's transformation process is fast, with minimal latency, integrating seamlessly into existing AI pipelines to maximize performance and throughput.
For custom quotes and offers, please reach out at contact@protopia.ai
Highlights
Unlock AI Data Potential Securely
Access sensitive data without compromising security, enhancing AI model accuracy with high-quality inputs.
Strengthen AI Security
Convert data into unintelligible forms to eliminate exposure risks throughout AI pipelines.
Stock Llama-3.1-8B-Instruct weights; the model you already know, now with support for protected prompts.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
This listing is free, so you pay only for the AWS compute you run. Pricing is organized by two choices. First, you pick an inference mode: batch, which processes grouped requests, or real-time, which serves live requests. Second, you pick an instance type from eight sizes, spanning CPU-based m5 instances and GPU-based p3, g4dn, and g5 instances. Every option bills per host hour (HostHrs), so cost scales with how long each instance runs. Larger or GPU-backed instances carry higher hourly rates. You match the mode and instance to your workload's speed and hardware needs.
Top-of-mind questions for buyers
What does one host hour (HostHrs) cover for billing?
One host hour is one running instance of the size you chose, billed for each hour it stays active. The count depends on how many instances run and for how long. Stopped instances stop accruing software charges, though underlying AWS resources like storage may still bill separately.
How do batch and real-time inference modes differ in how they use compute?
Batch mode processes grouped requests together, so an instance runs while it works through the queue. Real-time mode serves live requests, so the instance stays up to respond on demand. Both bill per host hour, so your choice affects how long instances run and accrue charges.
Do CPU and GPU instance choices affect what workloads I can run?
CPU-based m5 instances suit lighter workloads. GPU-based p3, g4dn, and g5 instances handle model inference that benefits from GPU acceleration. Stained Glass Transform runs on either CPUs or GPUs, so you pick the instance that matches your speed and hardware needs. Each size bills per host hour.
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An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Real-time inference
Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .
Batch transform
Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .
Version release notes
Update manifest to include weights.
Additional details
Inputs
Outputs
Usage instructions
Sample notebooks
Inputs
Summary
Body is fully compatible with OpenAI Chat Completions and Completions request bodies. Note that only Completions-style requests are compatible with prompt embeddings, using vLLM's prompt_embeds key in the JSON body.
Input MIME type
application/json, application/jsonlines
Real-time inference sample input data
{
"messages": [
{"role": "user", "content": "Why is the greenland shark the best shark?"},
],
"max_tokens": 150
}
Batch transform sample input data
{"messages": [{"role": "user", "content": "Why is the greenland shark the best shark?"},],"max_tokens": 150},
{"messages": [{"role": "user", "content": "Why is the greenland shark the best shark?"},],"max_tokens": 150},
{"messages": [{"role": "user", "content": "Why is the greenland shark the best shark?"},],"max_tokens": 150}
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Stained Glass Transform (SGT) by Protopia AI allows organizations to securely unlock high-value data for AI applications without compromising privacy. With lightweight, high-speed transformations, SGT strengthens data security, and expands compute options across diverse environments, while retaining model accuracy.
This SGT is specifically to be used with Llama-3.1-8B-Instruct
Please reach out to us at contact@protopia.ai for customized pricing before making a purchase.
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