kafeido.app integrates with the advanced open-source MLOps solution Kubeflow to streamline the orchestration of our domain-specific applications.
For public community collaboration or internal industrial collaboration, we use the OpenAPI specification integrated with SwaggerUI for its simplicity and consistent design, enhancing the user experience.
kafeido.app integrates with Hyper-Converged Infrastructure (HCI) and major cloud providers, enabling dynamic resource adjustments and horizontal scaling based on demand.
kafeido.app leverages the exclusive Micromodel Architecture (MMA) utility pattern (TW/USA) to minimize GPU reliance, ensuring efficient workload completion and reducing computing resource requirements by 20% to 50%.
kafeido.app supports streaming data from videos, audio, and text, providing rich input formatting for various purposes.
kafeido.app utilizes pipelines, a container orchestration flow, enabling customization. Domain owners only need to build their own pipelines for their applications, allowing everyone to work as usual.
We use openAI's whisper model to demonstrate its performance. The video shown on the left is the default whisper model performance, the video on the right is the mma-powered whisper model. Both services are running on single RTX 3090 GPU. and we can see the inference throughput is increased almost 50%.
Footprint-AI (信誠金融科技) is dedicated to developing machine learning platforms and providing AI-oriented software services. We specialize in large-scale data analysis and green computing, MLOps, and cloud native.
Bring Machine Learning to Everyone.
Footprint-AI focuses on a sustainable AI/ML platform, specializing in large-scale data analysis, MLOps, green software, and cloud native.