AIoT DevOps and Infrastructure: Difference between revisions
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[[File:2.3-DevOps-Enterprise.png|800px|frameless|center|Agile DevOps for Cloud and Enterprise Applications]] | [[File:2.3-DevOps-Enterprise.png|800px|frameless|center|Agile DevOps for Cloud and Enterprise Applications]] | ||
=== Agile DevOps for AI === | === Agile DevOps for AI === | ||
Many new concepts create challenges for AI DevOps | |||
* New roles: data scientist, AI engineer | |||
* New artefacts (in addition to code): Data, Models | |||
* New methods / processes: AI/data-centric, e.g. „Agile CRISP-DM“, Cognitive Project Management for AI (CPMAI) | |||
* New AI tools + infrastructure | |||
Additional AI DevOps challenges | |||
* Reproduceability of models | |||
* Model validation | |||
* Versioning: Models, code, data | |||
* Lineage: Track evolution of models over time | |||
* Testing and test automation: AI requires new methods and infrastructure | |||
* Security: Deliberately skewed models as new attack vector / adversarial attacks | |||
* Monitoring and re-training: Model decay requires constant monitoring and re-training | |||
[[File:2.3-DevOps-AI.png|800px|frameless|center|DevOps for AI]] | [[File:2.3-DevOps-AI.png|800px|frameless|center|DevOps for AI]] | ||
=== Agile DevOps for IoT === | === Agile DevOps for IoT === | ||
[[File:2.3-DevOps-IoT.png|800px|frameless|center|DevOps for IoT]] | [[File:2.3-DevOps-IoT.png|800px|frameless|center|DevOps for IoT]] | ||
=== Agile DevOps for AIoT === | === Agile DevOps for AIoT === |
Revision as of 21:15, 1 September 2020
Ignite AIoT: DevOps and Infrastructure
Agile DevOps for Cloud and Enterprise Applications
Agile DevOps for AI
Many new concepts create challenges for AI DevOps
- New roles: data scientist, AI engineer
- New artefacts (in addition to code): Data, Models
- New methods / processes: AI/data-centric, e.g. „Agile CRISP-DM“, Cognitive Project Management for AI (CPMAI)
- New AI tools + infrastructure
Additional AI DevOps challenges
- Reproduceability of models
- Model validation
- Versioning: Models, code, data
- Lineage: Track evolution of models over time
- Testing and test automation: AI requires new methods and infrastructure
- Security: Deliberately skewed models as new attack vector / adversarial attacks
- Monitoring and re-training: Model decay requires constant monitoring and re-training