Accelerate the development of AI Models through deployments and scaling of ML workflows on any infrastructure simple, portable and scalable
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 workflows. It provides a pipeline solution that enable automation within the Machine Learning dev lifecycle.
Automate deployments, training, etc.
Audit previous workflows
Share limited resources (GPU, TPU, etc.)
Build generic code to reuse
Integrate with any solution
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.
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.
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.
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