[Linux Academy] AIOps Essentials (Autoscaling Kubernetes with Prometheus Metrics) (John Marx)

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This course establishes a baseline for AIOps by utilizing Prometheus for managing time series metrics produced by Node Exporter and cAdvisor. The course guides the student through the fundamental concepts required for AIOps and the use of streaming metrics to influence autoscaling. The culmination of the course is the integration of the Prometheus rules with the Kubernetes APIServer to scale nodes in an active Kubernetes cluster.

Спойлер: Содержание складчины (файлы и папки)
00-introduction-to-author.mp4 [7m 179k 716]
00-introduction-to-author.srt [969]
01-introduction-to-this-course.mp4 [66m 625k 759]
01-introduction-to-this-course.srt [9k 55]
02-autoscaling-a-cluster-vs-scaling-an-infrastructure.mp4 [66m 612k 727]
02-autoscaling-a-cluster-vs-scaling-an-infrastructure.srt [10k 563]
03-the-case-for-aiops.mp4 [64m 817k 855]
03-the-case-for-aiops.srt [9k 797]
04-machine-learning-and-predictive-analytics.mp4 [32m 675k 25]
04-machine-learning-and-predictive-analytics.srt [4k 840]
05-prometheus.mp4 [23m 227k 861]
05-prometheus.srt [3k 202]
06-prometheus-node-exporter.mp4 [12m 219k 554]
06-prometheus-node-exporter.srt [1k 927]
07-google-cadvisor.mp4 [17m 124k 245]
07-google-cadvisor.srt [2k 506]
08-prometheus-node-exporter-and-cadvisor-demo-for-lab-prep.mp4 [36m 280k 395]
08-prometheus-node-exporter-and-cadvisor-demo-for-lab-prep.srt [5k 77]
09-data-taxonomy.mp4 [70m 827k 546]
09-data-taxonomy.srt [11k 542]
10-relabeling-with-prometheus.mp4 [54m 241k 282]
10-relabeling-with-prometheus.srt [7k 255]
11-aggregating-time-series-data.mp4 [46m 325k 619]
11-aggregating-time-series-data.srt [8k 92]
12-using-the-prometheus-api.mp4 [24m 974k 864]
12-using-the-prometheus-api.srt [3k 200]
13-the-problem-with-noise.mp4 [62m 345k 354]
13-the-problem-with-noise.srt [7k 873]
14-using-rules-in-prometheus.mp4 [30m 513k 844]
14-using-rules-in-prometheus.srt [3k 973]
15-using-dashboards-for-alerting.mp4 [35m 73k 316]
15-using-dashboards-for-alerting.srt [4k 489]
16-machine-learning-fundamentals.mp4 [93m 820k 497]
16-machine-learning-fundamentals.srt [13k 610]
17-using-python-to-predict-scale.mp4 [38m 781k 328]
17-using-python-to-predict-scale.srt [5k 366]
18-scaling-nodes-in-a-kubernetes-cluster.mp4 [95m 272k 477]
18-scaling-nodes-in-a-kubernetes-cluster.srt [12k 887]
19-scaling-a-hybrid-cloud-with-ml.mp4 [46m 528k 16]
19-scaling-a-hybrid-cloud-with-ml.srt [6k 138]
20-conclusion-and-next-steps.mp4 [20m 983k 253]
20-conclusion-and-next-steps.srt [2k 823]
21-credits-and-resources.mp4 [15m 972k 488]
21-credits-and-resources.srt [2k 197]
downloads
1122-interactive-diagram.pdf [15k 177]
transcript
00-introduction-to-author.txt [558]
01-introduction-to-this-course.txt [5k 175]
02-autoscaling-a-cluster-vs-scaling-an-infrastructure.txt [5k 800]
03-the-case-for-aiops.txt [5k 611]
04-machine-learning-and-predictive-analytics.txt [2k 729]
05-prometheus.txt [1k 839]
06-prometheus-node-exporter.txt [1k 40]
07-google-cadvisor.txt [1k 415]
08-prometheus-node-exporter-and-cadvisor-demo-for-lab-prep.txt [2k 898]
09-data-taxonomy.txt [6k 609]
10-relabeling-with-prometheus.txt [4k 90]
11-aggregating-time-series-data.txt [4k 553]
12-using-the-prometheus-api.txt [1k 803]
13-the-problem-with-noise.txt [4k 300]
14-using-rules-in-prometheus.txt [2k 134]
15-using-dashboards-for-alerting.txt [2k 480]
16-machine-learning-fundamentals.txt [7k 656]
17-using-python-to-predict-scale.txt [2k 983]
18-scaling-nodes-in-a-kubernetes-cluster.txt [7k 240]
19-scaling-a-hybrid-cloud-with-ml.txt [3k 381]
20-conclusion-and-next-steps.txt [1k 664]
21-credits-and-resources.txt [1k 276]

Объем: 918Мб.