Blog - posts for October 2020

Oct 26 2020

MLOps Demystified


As Machine Learning at an organization matures from research to applied enterprise solutions, there comes the need for automated Machine Learning operations that can efficiently handle the end-to-end ML Lifecycle.

The goal of level 1 MLOps (see figure) is to perform continuous training of the model by automating the entire machine learning pipeline which in turn leads to continuous delivery of prediction service. The underlying concept which empowers the continuous model training is the ability to do data version control along with efficient tracking of training/evaluation events. 

Oct 20 2020

Remote Technical Meeting


Date: October 20-21, 2020
Meeting: DECODER Technical Meeting
Place: Remote

This meeting - which was supposed to take place in Paris-Saclay at CEA List originally - turned to be virtual. Participants in the project are welcome to register through this private wiki event page to discover the meeting agenda and post WP presentations.

Check out more screenshots and photos.

Oct 08 2020

One Minute Video Teaser

This one minute teaser by UPV highlights the DevOps team current issues and how DECODER can contribute to solve them.

Oct 06 2020

Exploring Software Code to Understand Design Intentions

DECODER Poster October 2020
Date: October 6, 2020
Authors:  Virgile Prevosto (CEA List) and Olivier Bouzereau (OW2)
Publications: Rocking Robots, Medium

In order to ease collaboration between programmers, testers, and system operators, the DECODER platform provides deep analysis of source code repositories involved in business applications or embedded systems.

The goal of DECODER is to improve the efficiency of development, testing, verification and validation through a centralized platform. This platform is a work in progress, as it consolidates various sources of information to keep the knowledge about source code repositories, libraries and components in sync. An original feature of the project is that it considers software code as a form of natural language. Therefore, it relies on NLP (Natural Language Processing) tools and semi-formal abstract models to understand the intentions and the properties of source code.