Features - Machine Learning 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

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

Get Started

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

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

screenshot of ProActive resource manager

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

screenshot of ProActive workflow with some connectors

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.)

screenshot of some ProActive node selectors

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

screenshot of ProActive feature to for environment within a Docker container

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

screenshot of Activeeon hub where package, connectors, plugins can be shared

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

error management icon

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

monitoring icon

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

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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

lifecycle icon

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

rest api icon