Machine Learning Open Studio

Accelerate the development of AI Models through deployments and scaling of machine learning workflows on any infrastructure

Accelerate the development of AI Models

Simplify, accelerate, industrialize machine learning with an open platform. Seamlessly execute at any scale in production with any data source, on any infrastructure.

The Machine Learning Open Studio from Activeeon empowers data engineers and data scientists with a simple, portable and scalable solution for machine learning pipelines. It provides pre-built and customizable tasks that enable automation within the machine learning development lifecycle.

Consistency and Repeatability

consistency and repeatability schematic

Reusable, standartized pipelines
Auto ML and incremental AI
Automate deployments and training
Traceability over code and data
Job analytics to ease model evaluation

Scalability and Portability

scaling and portable schematic

Run on any infrastructure: on-premises or cloud
Share limited resources: execution over CPU, GPU, FPGA
Scale up complex AI and big data applications Use ProActive portals directly in Jupyter

Open and Loosely coupled

loosely couple and reusable schematic

Reusable code to integrate with any solution
ML & DL model management and version control
Python integration with a dedicated API
Execution from Jupyter with ActiveEon Kernel
Traceability over code and data

Consistency and reliability

The consistency and reliability offered by Activeeon workflows enable data engineers and data scientists to create and automate pipelines. The consistency provided ensures that results are equals across execution. The reliability lets data scientists and data engineers confidently execute machine learning pipelines.

Use cases

  • Automate hyperparameters identification and tuning. With Activeeon, create a complete pipeline that will parallelize multiple ML model trainings and feed the results to AutoML libraries to generate a new batch of hyperparameter tests.
  • Build standard pipelines to extract data, transform and overall prepare it for your machine learning training models.
  • Review successful workflow runs and understand what has worked.
consistency and reliability schematic

Scaling and portability

Portability is key to ensure no vendor lock in and promote collaboration between users. It is also critical to scale. The workflows and algorithms created need to access any infrastructure setup (on-prem, hybrid, multi-cloud, HPC, etc.) and leverage the whole compute capacity from CPU to GPU / TPU / FPGA.

Activeeon includes a resource manager that abstracts away the resources and offers this portability. Some smart policies can also be configured to trigger auto-scaling based on the actual scheduler queue.

Use cases

  • Build a successful devops pipeline with dev, staging, QA and prod environments,
  • Run distributed pipelines at any scale to get results faster.
  • Run workflows in parallel to test multiple options and validate hypothesis at scale.
  • Move your work on infrastructure with GPUs or specific hardware to train your model faster.
  • Share pipelines with coworkers, ask advices, share best practices.
scaling and portability schematic

Openness and loosely coupled tasks

The machine learning ecosystem is constantly evolving and its open source community is strong. The ability to leverage those contributions is key to ensure constant up to date technics and best performances. Activeeon is open from end to end and support those needs.

Moreover, some steps of the machine learning process are quite repetitive and could be made generic. Activeeon includes a catalog solution that enables sharing, versioning and easy reutilization.

Use cases

  • Select the libraries that you are most comfortable with.
  • Build catalog of reusable code to help you get started and follow best practices.
  • Edit blocks for faster iterations between algorithms, data sources, data transformation, etc.
consistency and reliability schematic
  • Make deployments and scaling of machine learning (ML) workflows on any infrastructure simple, portable and scalable

  • Provide a straightforward way to deploy open-source systems for ML to diverse infrastructures (local, hybrid, multi-cloud)

  • Provide a pipeline solution to enable automation within the Machine Learning dev lifecycle

Legal & General
Capgemini
INRA
L'Oréal
Home Office
CNES

Proactive directly integrates
and offers ready-to-use libraries

BigDL
BigDL
CNTK
CNTK
Caffe
Caffe
DLib
DLib
G4j
G4j
H20
H20
Keras
Keras
MXNet
MXNet
PyTorch
PyTorch
Spark Mlib
Spark Mlib
TensorFlow
TensorFlow
cognitive services
cognitive services
jupyter
jupyter
pandas
pandas
scikit learn
scikit learn
More connectors and libraries

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Machine learning open studio product sheet
Machine Learning Open Studio

Accelerate development and deployment of artificial intelligence models more...

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