Panel:

MLOps Platform

Scalable

Scalable

Scalable

Scalable

Portable

Portable

Portable

Portable

Secure

Secure

Secure

Secure

Unlock the Full Potential of Your AI Models

Panel is a Kubernetes-based, cutting-edge platform empowering enterprises to build, deploy, and scale AI solutions effortlessly.  

 

Our scalable, secure, and portable solution unifies data scientists, engineers, and operators in a collaborative environment, accelerating the development, deployment, and management of AI models that drive business growth and innovation. 

The modular and scalable Panel approach can be implemented on-prem or private cloud environments, which streamlines development and operational utility of models through data and machine learning pipelines. 

 

Our approach is based on open architecture design, which scales operationally at low cost and avoids commercial vendor lock or monolithic software solutions.  

Model Development

Model Development

Integrated development environment (IDE) for data scientists to build, train, and deploy AI models using popular frameworks such as TensorFlow and PyTorch.

Integrated development environment (IDE) for data scientists to build, train, and deploy AI models using popular frameworks such as TensorFlow and PyTorch.

Model Deployment

Model Deployment

Automated deployment of models to production environments, with support for

containerization and orchestration using Kubernetes.

Automated deployment of models to production environments, with support for containerization and orchestration using Kubernetes.

Automated deployment of models to production environments, with support for

containerization and orchestration using Kubernetes.

Model Monitoring

Model Monitoring

Real-time monitoring and logging of model performance, with alerts and notifications

for anomalies.

Real-time monitoring and logging of model performance, with alerts and notifications for anomalies.

Model Management

Model Management

Centralized management of models, data, and infrastructure, with support for

version control and collaboration.

Centralized management of models, data, and infrastructure, with support for version control and collaboration.

Centralized management of models, data, and infrastructure, with support for

version control and collaboration.

Key Benefits

Continuous Delivery

Code changes are automatically built, tested, and prepared for production during short iterations

Continuous Training

Automatically retraining models in production so that the end result is ready to deploy without manual interventions

Continuous Testing

Evaluating the product at every stage of the lifecycle facilitates continuous delivery

Continuous Monitoring

Monitoring at all stages ensures models perform in the wild and revert to previous iterations in case of failure

Improve model quality and reliability with real-time monitoring, logging and alerts for anomalies that enable prompt action to address issues.

Reusable Infrastructure

Standardizing infrastructure prevents starting over from square one each time a new model is necessary

Improve model quality and reliability with real-time monitoring, logging and alerts for anomalies that enable prompt action to address issues.

Reproducible Environments

Ensuring versioning, fault tolerance, and governance without sacrificing fast, efficient development

Improve model quality and reliability with real-time monitoring, logging and alerts for anomalies that enable prompt action to address issues.