Use it carefully, as it can persuade the operator that a mistake is correct. Furthermore, excessive use might numb the operators problem solving ability.
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.
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.
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.
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.
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.
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
What do you think about AI for Kubernetes? Is it a gimmick or something we should use today or something that will become useful in the future?
If it reduces operational tickets and stupid questions about why is my pod in crashloopbackoff then yes :)
i have used k8sgpt to troubleshoot issues in our cluster, including one that was a blocker for several weeks, very amazing tooling.
Use it carefully, as it can persuade the operator that a mistake is correct. Furthermore, excessive use might numb the operators problem solving ability.
You are a very effective communicator. Great video once again.
I think the analysis is skewed because of the gpt-3.5 model, instead of 4.
Great video Victor, keep going! 👌
excellent explanation.💐
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.
any github page?
Viktor you are the man, I don´t care :)
k8sgpt looks like a dummy data pipeline (for now) from k8s cluster to a openai backends and presents the response from openai on a cli level.
Very Nice video
Great analysis. Can't wait to see what more it will have to offer in future as it grows.
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.
Definitely, some has to get an ass chewing from management when something goes wrong
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.
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.
# til
Very interested in all your vids as i'm not a devop myself to get an experienced view of already available solutions. Keep up the good work
Btw thank you for your job, you explain it very well
Fantastic and insightful videos as always, Viktor!!!
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.
In the nutshell, all it does is send events to chatgpt and output the results.
The master of the whole field ... such a great tutorial like always
Since we are talking about AI, is there any chance of a video on gpt engineer?
Great video, BTW
If you mean an engineer working directly with language models, that's not one of my main areas of expertise 😔
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
Thank you Good job.💥
Another one
Awesome thanks
First viewer
No issue with mentioning AI , it’s no longer hype at this point 🎉