04-801-V4   Methods and Tools for AIOps: Observability

Course description

The course is focused on "Observability" for Cloud-based applications covering instrumentation and capture of data needed to apply AI methods for anomaly detection and correction. The class will present basic concepts of DevOps including Docker, CI/CD pipelines, and the microservices architectures used in hybrid cloud deployments. Methods for instrumenting these applications for observing their behavior and storing/displaying such data are core topics covered. This course builds required skills for the Fall semester AIOps class. Students will apply tools including Docker, time series databases such as Prometheus, dashboards such as Grafana, UI tools such as Dash, platforms for load and fault injection such as Locust, and core HTTP frameworks for APIs such as Flask.

Learning objectives

In this course students will:

• Deploy applications into a microservices platform based on Docker containers
• Understand the methods available for collecting, storing, and displaying real-time performance data for services and models
• Acquire techniques for generating deviations in performance based on load or simulated faults

Outcomes

By the end of this course, students will be able to:

• Understand the need for automated methods in maintaining a high level of service availability
• Deploy and manage containerized components and models in a microservices runtime
• Instrument components and models for real-time data collection for analysis and visualization
• Apply methods for anomaly generation
• Build simple dashboards for displaying application status and performance

Prerequisites

Strong background in Python programming