cnvrg.io is now Intel® Tiber™ AI Studio
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A Full Stack Machine Learning Operating System

Everything an AI developer needs to build and deploy ML

Get immediate access to managed compute or bring your own

Your entire AI infrastructure stack in one place

Intel® Tiber™ AI Studiois the world's most flexible end-to-end machine learning operating system built to empower AI developers to build high impact models, faster, on any AI infrastructure. With cnvrg.io, AI developers are given the freedom to run AI workloads where it is faster and most cost effective, in half the time.
Automate

Build frictionless machine learning pipelines in just a few clicks

  • End-to-end

    • Instantly build production ready pipelines with custom or pre built ML components

  • Portable

    • Simplify engineering heavy tasks with MLOps and a container-based infrastructure

  • Optimized

    • Orchestrate any ML task to run on any AI stack to maximize performance

Orchestrate

Aggregate and deploy your AI infrastructure stack from one launch pad

  • Visibility & Control

    • Maximize server utilization across all ML runs with a 360-view of your entire AI infrastructure stack by project, workload, container and job

  • Dynamic Orchestration

    • Mix-and-match on premise and cloud resources for heterogenous ML pipelines with native Kubernetes cluster orchestration and meta-scheduler

Research

Experiment freely with a full view of your organizations ML projects

  • Collaborate

    • Easily share and collaborate on ML projects with an interactive and dynamic interface

  • Track

    • Store models and meta-data with a track log, including parameters, code version, metrics and artifacts production models

  • Analyze

    • Compare and visualize training and deployment metrics of your experiments in real time

Deploy

Instantly deploy AI models to production

  • Production ready

    • Deploy your models at hyperspeed with autoscale and advanced built in monitoring capabilities

  • Compliant

    • Track inference on top of models and automatically update models in real time to maintain performance

  • Delivery

    • Publish models on Kubernetes clusters in one click via web-service, Tensorflow serving, Batch inference, RabbitMQ, Kafka Stream

Manage

Connect any data source and control your machine learning datasets in one place

  • Easy access

    • Access all of your teams data in one central hub to use for any project or task

  • Support all data types

    • Import data in any format and integrate it into any experiment or analysis session

  • Machine learning ready

    • Manage datasets with tagging, automated versioning and querying capabilities