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Description

Spherical Defense is an innovative API security solution that leverages deep unsupervised learning techniques to safeguard your APIs effectively. The Spherical Defense Express variant is designed for deployment on AWS and can be downloaded in just one minute, starting its protective measures within two hours at an economical rate of $1 per hour. After setting up your Spherical instance, it begins monitoring API traffic right away and continues to do so until it gathers enough data for initial model training. Once approximately 16,000 requests are processed, the system transitions to the training phase, where it refines its security model over about six hours before undergoing evaluation. As the system continues to receive new data, it adapts by training additional models to reflect changes in your API's traffic patterns over time. The process of training and adapting ensures ongoing protection, making it a dynamic and responsive security solution for your APIs. After the evaluation of the trained security model, the system is better equipped to handle future threats effectively.

Description

Accelerate the building, training, and deployment of models at scale through a fully managed infrastructure that provides essential tools and streamlined workflows. Launch personalized AI and LLMs on any infrastructure in mere seconds, effortlessly scaling inference as required. Tackle your most intensive tasks with batch job scheduling, ensuring you only pay for what you use on a per-second basis. Reduce costs effectively by utilizing GPU resources, spot instances, and a built-in automatic failover mechanism. Simplify complex infrastructure configurations by deploying with just a single command using YAML. Adjust to demand by automatically increasing worker capacity during peak traffic periods and reducing it to zero when not in use. Release advanced models via persistent endpoints within a serverless architecture, maximizing resource efficiency. Keep a close eye on system performance and inference metrics in real-time, tracking aspects like worker numbers, GPU usage, latency, and throughput. Additionally, carry out A/B testing with ease by distributing traffic across various models for thorough evaluation, ensuring your deployments are continually optimized for performance.