Accelerate ML Model Delivery with A360 AI
Gain Speed of Automated
Deploy Machine Learning Models at the speed, scale, and security of your existing DevSecOps processes by leveraging A360 Deploy for automated GitOps-based CI/CD deployments.
Realize greater ROI in your AI projects by increasing collaboration across business users, data scientists, and machine learning engineers which are now aligned closely on business goals leveraging A360 Build and Monitor.
Enhance Collaboration to Improve Return on Investment
Run your AI workloads with scale and security on the infrastructure of your choice across clouds and on premises by leveraging A360 Deploy and Operate which is open, secure, and hybrid enabled.
Machine Learning Development and Deployment Made Simple
What Makes the A360 AI Platform Unique?
Open & Cloud-Agnostic Architecture
Cloud-specific AI tools are costly, restrictive, and difficult to use. A360 is open and cloud-agnostic, so you can plug and play with the software and infrastructure your business already uses. With A360 Model Development Kit (MDK), use your favorite Jupyter Notebook environment – Ours or Sagemaker, Colab, Azure ML, etc.
Model Deployment as Code (MDAC)
Deployment and change management of AI applications can take weeks (if not months) without integration into Enterprise CI/CD toolchain. A360 provides unique model deployment as code technology called Starpack that integrates with your existing pipelines. Also, integrate with data and ML platforms of your choice – Snowflake, Databricks, C3.ai, H2O.ai, Domino, etc.
Explore the A360 AI Delivery Platform
Cut the time to bring AI models from experiment to production in half or import your existing AI models. Automated experiment and run tracking make model selection and auditing easy with A360 Build.
A360 Build isn’t just for training models. It drives collaboration with data science, machine learning, devops, and business teams. You’ll also get workspace management, data connectors, and a laser focus on business value.
A360 Deploy allows you to reliably version and package models for deployment across your infrastructure with minimal effort and repeatable results using model deployment as code based on A360 Starpack.
Deploy automatically across Cloud, Edge, and On-Prem environments with version control, real-time system performance, and the peace of mind that comes with utilizing secure containers with A360 Deploy.
Use your own Kubernetes cluster for model serving or leverage our fully automated and scalable cloud-based A360 Operate environment. Choose your base image, select from CPU or GPU requirements, and start serving AI applications securely.
A360 Monitor provides a wide range of metrics including Concept Drift, Data Drift, Uptime, and Availability as part of a customizable dashboard which lets Data Scientists and ML Engineers focus on the metrics that matter most.