Development
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- 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:
- More emphasis on information pipeline management; data collection, preparation, feature determination, and pipeline configuration management.
- 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
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- 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
AIOps
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- What is so Special About AIOps for Mission Critical Workloads? | Rebecca James - DevOps
- What is AIOps? Artificial Intelligence for IT Operations Explained | BMC
- AIOps: Artificial Intelligence for IT Operations, Modernize and transform IT Operations with solutions built on the only Data-to-Everything platform | splunk>
- How to Get Started With AIOps | Susan Moore - Gartner
Machine learning capabilities give IT operations teams contextual, actionable insights to make better decisions on the job. More importantly, AIOps is an approach that transforms how systems are automated, detecting important signals from vast amounts of data and relieving the operator from the headaches of managing according to tired, outdated runbooks or policies. In the AIOps future, the environment is continually improving. The administrator can get out of the impossible business of refactoring rules and policies that are immediately outdated in today’s modern IT environment. Now that we have AI and machine learning technologies embedded into IT operations systems, the game changes drastically. AI and machine learning-enhanced automation will bridge the gap between DevOps and IT Ops teams: helping the latter solve issues faster and more accurately to keep pace with business goals and user needs. How AIOps Helps IT Operators on the Job | Ciaran Byrne - Toolbox
