Tuesday, December 5, 2017

Agility for Data Scientists

The process followed by data scientists can be simplified by the diagram below. As shown, it is an iterative process where hypothesis are made and the model improved incrementally. Multiple platforms are competing on supporting data scientists’ needs but too often focus on standard workflows and methods whereas each use case is different and require specific development.

At Activeeon, our data scientist team is focusing on building templates that can be used and tweaked easily at each iteration.

You can easily edit the code we provide as template. See below the code of the task named: Python_Task3.

You can import/export models and data sources. See below a few templates available to get you started as fast as possible.

You can use the language of your choice. Activeeon includes features to share variables across tasks and share file easily.

Finally, the scheduler which will execute those workflows includes features such as error management, prioritization, log fetching, etc. Data scientists can focus on their code and reliably expect results.

To conclude, Activeeon’s solution is ideal to meet needs for Machine Learning, Deep Learning, Reinforcement Learning, etc. Data scientists access a production tool while benefiting from efficient flexiblity.

A video has been developed to show the capabilities of Activeeon and its integration with tools such as Visdom.

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