Use it carefully, as it can persuade the operator that a mistake is correct. Furthermore, excessive use might numb the operators problem solving ability.
This looks like an interesting project. Would give it a try. Hopefully it will improve over period of time and your dream will come true to fix issue automatically.
I would say we as DevOps engineers are will be around for at least next 10-15 years even if demand of the other specialities like QA or Programming will be less and less.
Glad you did it. Can't be avoided. Please embrace 😊. But don't you mean GenAI! Kubeflow, Sheldon, spark, jupyternotes were already used in K8s. AI is not a strange workload. Using GenAI to develop manifests, test them, optimise them would be easy and nice
Yeah. This time I did not go through AI/ML workloads running in Kubernetes or helping write manifests but rather using AI to analyze the state of Kubernetes resources.
@@DevOpsToolkit Still I am glad indeed you are exploring and sharing your insights into this area🤗 The analysis would be better if it is customized with a good data source as you started in the beginning, the monitoring tools, the console, in addition to some uptodate FAQ, or learned data about the issues would have produced much more superior and informative results
Tremendous potential here. It would be interesting to see if it can be integrated into Gitops. Say, an option to create a PR for a fix instead of doing it automatically and then submitting it to a CI/CD pipeline.
Is it really Aİ behind? I think one good thing would be to take good and bad resources (those with noticed errors on it), train the AI model and then use that model on your cluster. But to do that, you must get old events from a lot of clusters through a lot of companies. Which is not that easy.
Your videos are awesome but for me this is not worth using. I made lots of custom controllers that do far better than this without using AI. AI is nowadays just unwanted hype. It's still not good. Even chatgpt used to repeat things at one point.
