What drives AI and ML projects is not programmatic code, but rather the data from which learning must be derived. What is needed is a project management methodology that takes into account the ...

Defining an agile process for data science and dataops. Applying agile methodologies to the analytics and machine learning lifecycle is a significant opportunity, but it requires redefining some ...

What makes Agile difficult for some managers to grasp is that it’s not just a methodology or process that can be implemented within a firm’s current assumptions.

Innovation thrives on speed and adaptability. Here’s a simple four-step process to streamline decision-making and execution.

From planning to development to management to security, at CA Technologies we create software that fuels transformation for companies in the application economy. By Ayman Sayed, President and ...

Agile methodologies address these shortcomings of top-down project management. They require engineers to step into agile roles, participate in ceremonies, and use agile tools to understand a new ...

“The Agile BioFoundry’s objective is to develop and deploy technologies that enable commercially-relevant biomanufacturing, including using advanced machine learning methods in non-model microbes.

In accordance with Agile principles, in-person classroom interactions and face-to-face instruction are crucial and strongly preferred over technological or distance learning methods. Extensive group ...

Chopra said the Purdue team’s human-interpretable machine learning approach, introduced as chemical reactivity flowcharts, can be extended to explore the reactivity of any MCR or any chemical reaction ...