Development

Youtube search... ...Google search

• Case Studies
• 6 Ways AI Transforms Software Development | Mariya Yao - MetaMaven
• Machine Learning vs Traditional Programming | Oleksii Kharkovyna - Towards Data Science - Medium
• Web-Based Monte Carlo Simulation for Agile Estimation
• Why AI & ML Will Shake Software Testing up in 2019 | Oleksii Kharkovyna - Medium
• Track: Solving Software Engineering Problems with Machine Learning | Cyril Magnin III - QCon.ai
• Automated Machine Learning (AML) - AutoML
• Re-imagining developer productivity with AI-assisted tools | Amanda Silver - Microsoft ...AI-assisted IntelliSense GPT-2 transformer

Major differences:

1. More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management.
2. Developing a machine learning application is more iterative and explorative process than traditional software engineering. Learning / Testing / Validation of models is an upfront task

Developing a machine learning application is even more iterative and explorative process than software engineering. Machine learning is applied on problems that are too complicated for humans to figure out (that is why we ask a computer to find a solution for us!). Differences between machine learning and software engineering | Antti Ajanki - Futurice

Agile

• AI-Based Framework for Agile Project Management | Sandeep Aspari - Hackernoon
• How To Achieve Effective AI-Powered Agile Project Management | Martin F.R - Analytics India
• Using Artificial Intelligence to Boost Agile/DevOps Efficiency | Kristof Horvath
• AI-Based Framework for Agile Project Management | Stephanie Donahole - ReadWrite
• 9 Ways To Implement Artificial Intelligence and Agile-Powered Management in Software Development | Chandresh Patel - DZone
• Agile is great — Scrum is OK-ish; If you work on software you should really understand the difference | Sean Dexter - Medium