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]
