First question: How do you define AIOps?
Helen Beal 🐝
It helps orchestrate speed.
AI Ops uses artificial intelligence to enhance traditional IT and cloud native operations through data aggregation, automation, correlation, pattern recognition and orchestration.
Application of machine learning practices to the field of IT Operations
Sharath Dodda, PMP, PSM, ITIL, TOGAF Trained
Power of AI in Ops
It enhance predictive & adaptive decision making #AI
Marc Hornbeek, DevOps_the_Gray esq.
MLOps is the capability to apply learning models to large datasets for IT applications. AIOps is the capability to generate decisions based on heuristic algorithms operating on large data sets and make use of MLOps to do that.
Marc Cluet #BLM
Great way to be able to be able to forecast and identify issues relating to performance and bottlenecks in your applications #SKILupDays #AIOps
in simple words, Artificial Intelligence for the Information Technology Operations. #AIOps #MLOps #DevOps #SKILupDay
AIOps: For me AI-based Algorithms for ITOps use cases with goal to reduce MTTR, MTTD by iteratively increasing the quality of resolutions
I would define AIOps is using of the large amount of data that is generated and collected in Operations to help making the systems more stable and reliable.
@DEVOPSINST, #SKILupDays #AIOps: AIOps as Gartner defines stands for "Artificial Intelligence for ITOperations". It also refers to multi-layered technology platforms that automate and enhance IT operations through analytics and machine learning (ML).
AIOps mean replacing your developers with agents who can code directly for you, don't take vacations, and only rarely ask for raises
@mhexcalibur As long as AI generates some type of decision, does it matter what basis is used? I suppose all decisions are algorithmic but is there a need to check the algorithm or just inputs/outputs to AI-box?
data versioning and model repo will be some new features for DevOps