Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused ...

Feedback should drive data visualization improvements, machine learning model recalibrations, data quality increases, and data governance compliance. Applying agile methodologies to the analytics ...

Remember also to document every step of the way so you can share your work with your co-developers. To begin the machine learning method, first, define the problem you’re trying to solve in its ...

But, what about the application of AI and ML to agile development ... and adopt newer methods. The practical application of artificial intelligence and machine learning will become a standard ...

“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 microbe ...

Beyond executing these implementation steps, the paper examines what it takes to be successful in Agile risk management, including these key factors: Integration with Agile methodology Tailoring ...

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 ...

He is also the founder of AD3 Analytics, a start-up that developed the OML methodology for supply chain management. Rohan Deshpande is a machine learning scientist at Cerebras Systems and a former ...