ML Open Studio

Machine Learning from dev to production

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.

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Customer Machine Learning Studio


ML-OS Screenshot
  • Visualize workflows and their dependencies
  • Setup custom menu for simple drag and drops
  • Share workflows or tasks
  • Customize code on imported tasks for better results and performances

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Ease data connection


Focus on what is important with pre-made connectors to data sources

Connect to the most common sources
(Hadoop, SQL, Cassandra, MongoDB, S3, …)
and formats (CSV, Excel, SAS, JSON, …)

data connectors

For Devs & Data Scientists

Agility and Openness


Develop Once, Deploy Anywhere

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
Data source connectors

Scripted resource selection

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
Simplified Docker Integration

Develop with any library and devops tools

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

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

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

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

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

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

For more information

Request a call, a demo
& ask questions.

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