ProActive | ML Open Studio

Build, Deploy, Automate Machine Learning at Scale

The Machine Learning Open Studio (ML-OS) from Activeeon is a complete platform for machine learning industrialization. From build and deployment to automation and production offering governance and control through your workflows.

Get Started 3 step tutorial

For Devs & Data Scientists

Agility and Openness

Develop Once, Deploy Anywhere

Become agnostic to the resource from dev to prod. Deploy Machine Learning in production in minutes.

Benefit from an abstraction layer on the resource
Run workloads locally, on-prem, in the cloud (Azure, AWS, Gcloud, OpenStack, VmWare, etc.)
Move to production in minutes and not hours or days

Data source connectors

Connect to the most popular data source with a simple drag & drop

Filesystem, FTP, HTTP, SSH, SFTP
PostgreSQL, MySQL
Analytic SQL (Greenplum, etc.)
NoSQL (MongoDB, Cassandra, Elasticsearch)
Hadoop (HDFS)
Cloud (S3, blob, buckets)
Scality

Scripted resource selection

Select dynamically the resource required: GPU, RAM, OS, lib, etc.

Select the most relevant resource based on hardware requirements (GPU, RAM, etc.)
Based on location (Azure, AWS, OpenStack, VmWare, On-Prem, In France, In US, etc.)
Based on variable information (latency, bandwidth, etc.)
Based on OS configuration (Docker enabled, Python3 enabled, etc.)

Simplified Docker Integration

Share files and variables across containers

Propagate variables through containers
Share files through containers via our Dataspace
Ensure all the libraries are available in any environment

Develop with any library and devops tools

Enjoy a fully open system and leverage the best libraries. Setup a complete machine learning orchestration system.

Integrate with any Machine Learning and Deep Learning libraries
Extend the studio with custom packages from your team
Extend the studio with our community packages on the hub

For Production

Orchestration and Control

Error management and alerts

Setup simple recovery rules in case of errors

Advanced error management policies (kill job, suspend dependent tasks, ignore, etc.)
Setup alerts on error

Schedule and monitor workloads

Plan jobs, add execution exceptions and monitor them

Setup cron expression to repeat execution
Setup periods of non execution (e.g. for maintenance)
Setup additional execution (e.g. for bank holidays)
Monitor all jobs from a single interface

Fast time to result with distribution system and cloud bursting

Improve time to result with integrated control structures

Run algorithms in parallel
Leverage multi-threading at ease
Prioritize important reports

Lifecycle management of services and application

Manage lifecycles required for jobs or for cost purposes

Automatically trigger servers such as Visdom for visualization
Monitor service utilization and resizing

Comprehensive Rest API

Integrate and build with a completely open solution

Trigger workflow execution, prioritization, etc. from external applications
Monitor execution from thrid party services

ML Open Studio