Chart

About Chart

Chart: Deploy High-Performance ML Models with Ease

Machine learning (ML) models have the potential to revolutionize industries, but deploying them into production remains a challenge. Chart offers a seamless solution for deploying high-performance ML models, providing an interactive canvas to design and deploy system architectures in minutes.

The Problem: Complexities of MLOps

Deploying ML models into production-grade inference APIs is a daunting task, often involving intricate configurations and management. Moreover, popular models can be slow to serve requests, resulting in poor user experiences.

Many teams want to integrate AI features while maintaining low latency and adhering to strict data sensitivity requirements. However, handling MLOps complexities in-house can be overwhelming.

The Solution: Chart's Simplified ML Model Deployment

Chart simplifies ML model deployment by packaging models into high-performant C++ servers and deploying them directly to your cloud account (AWS or GCP). By hiding the complexities of packaging models (Dockerfiles, Flask servers, CUDA versions, etc.), Chart allows companies to self-host high-performance ML models while keeping data within their cloud provider.

Chart's process involves:

• Compiling the model into CUDA/HIP optimized C++ code
• Packaging the model with an HTTP server into a Docker image
• Provisioning an auto-scaling Kubeflow cluster with optimized configuration files

The result is a dedicated, high-performant inference API in the user's VPC, a UI for API interaction, and a Grafana dashboard for monitoring.

Key Features of Chart

Chart offers a streamlined approach to deploying ML models:

• Cloud Provider Integration: Integrate your cloud account (AWS or GCP) through IAM roles with the correct permissions.
• Open-Source and Proprietary Model Support: Choose from a catalog of open-source models or upload your own proprietary models.
• Simplified Deployment: Click «Deploy» and let Chart handle the complexities of model packaging and provisioning.
• Optimized Performance: Experience lightning-fast inference with CUDA/HIP optimized C++ code and GPU compute cost minimization.

Benefits of Chart

By using Chart for ML model deployment, users can:

• Save time: Reduce the time spent on complex MLOps tasks.
• Maintain control: Keep data within your cloud provider, ensuring data sensitivity requirements are met.
• Improve performance: Experience faster inference through optimized C++ code and GPU utilization.
• Simplify management: Monitor deployments with an easy-to-use Grafana dashboard.

Applications of Chart

Chart is ideal for industries and applications that require high-performance ML model deployments, such as:

• Healthcare: Improve diagnostics and patient care with rapid ML-based analysis.
• Finance: Enhance fraud detection and risk management with faster ML model processing.
• Retail: Optimize inventory management and customer recommendations using ML insights.
• Manufacturing: Boost production efficiency with real-time ML-based monitoring and analysis.

Conclusion: Deploy ML Models Effortlessly with Chart

Chart revolutionizes ML model deployment by abstracting the complexities of MLOps, transforming models into high-performant C++ servers, and deploying them directly to your cloud account. Experience seamless integration, rapid deployment, and optimized performance with Chart, enabling you to focus on enhancing your products and services with cutting-edge AI features.