Monitoring, Docker & AI

Role-Specific Focus Areas

This module targets skills frequently requested in DevOps Engineer roles in the mobility/automotive sector — covering application monitoring, operational troubleshooting, Docker workflows, DevOps toolchains, and leveraging AI for engineering productivity. These are the "glue" skills that complement your infrastructure knowledge.

1. Application Monitoring & Observability

As a DevOps Engineer you're responsible for the smooth operation of the system. This means knowing when something breaks before users report it. AWS provides three key monitoring pillars:

The Three Pillars

CloudWatch — Core Concepts

CloudWatch Logs — Application Logging

Centralize your application logs so you can search, filter, and alert on them:

2. Operational Troubleshooting

Identifying and resolving operational issues is a core DevOps responsibility. Here's a systematic approach:

The Troubleshooting Framework

1. DETECT   → Alarm fires or user reports issue
2. TRIAGE   → Severity? Blast radius? Who's affected?
3. DIAGNOSE → Check metrics, logs, traces, recent changes
4. RESOLVE  → Apply fix (rollback, scale, config change)
5. POSTMORTEM → Document root cause, add monitoring to prevent recurrence

Common AWS Issues & Resolution

# Check app status
pm2 list
pm2 logs sandbox --lines 50

# Check system resources
top -bn1 | head -20
df -h
free -m

# Check network connectivity to RDS
nc -zv sandbox-db.xxxxx.rds.amazonaws.com 5432

# Check recent deployments
ls -la /opt/codedeploy-agent/deployment-root/

# View environment variables
cat /home/ec2-user/sandbox-app/app/.env

3. Docker — Container Fundamentals

Containers package your application with all its dependencies into a portable unit. Docker is the most common container runtime and a core DevOps skill.

Key Concepts

Dockerfile Example

# Multi-stage build: build stage
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .

# Production stage: minimal image
FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app .
EXPOSE 3000
USER node
CMD ["node", "server.js"]

Essential Docker Commands

AWS ECR — Private Container Registry

Elastic Container Registry (ECR) is AWS's private Docker registry. Store images securely and pull them into ECS, EKS, or EC2.

# Authenticate Docker to ECR
aws ecr get-login-password --region us-east-1 | \
  docker login --username AWS --password-stdin \
  ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com

# Build and tag
docker build -t sandbox-app .
docker tag sandbox-app:latest \
  ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/sandbox-app:v1

# Push
docker push \
  ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/sandbox-app:v1

Docker vs VM Comparison

4. DevOps Toolchain & Workflows

DevOps isn't just infrastructure — it's the processes and tools that enable teams to deliver software reliably. Here are the key workflow tools you'll encounter.

Git Branching Strategies

GitHub Flow (recommended for most teams):

main ──────●──────●──────●──────●──────
            \              /
feature/ABC  ●────●────●──┘  (PR + merge)
                    ↑
              Code review

Jira & Confluence Integration

Jira manages tasks/sprints; Confluence manages documentation. Together they form the project management backbone:

Incident Management Process

1. ALERT    → CloudWatch alarm / PagerDuty fires
2. RESPOND  → On-call engineer acknowledges (< 15 min)
3. COMMUNICATE → Update Jira ticket + Slack channel
4. MITIGATE → Rollback, scale, or hotfix
5. RESOLVE  → Confirm service restored
6. POSTMORTEM → Blameless doc in Confluence:
               - Timeline of events
               - Root cause (5 Whys)
               - Action items to prevent recurrence

5. Generative AI for DevOps Engineers

Employers increasingly expect familiarity with AI tools. The goal isn't to replace engineering judgment — it's to accelerate routine work and augment decision-making.

Practical AI Use Cases in DevOps

Effective Prompting for DevOps

The quality of AI output depends on your prompt quality. Use this structure:

ROLE:    "You are a senior DevOps engineer specializing in AWS"
CONTEXT: "I have an EKS cluster running 3 nodes with nginx ingress"
TASK:    "Write a Kubernetes HPA manifest that scales based on
         request rate using custom metrics"
FORMAT:  "Provide a complete YAML manifest with comments explaining
         each field"

You are a DevOps engineer working with AWS. I have an RDS PostgreSQL
instance called sandbox-db. Generate a CloudWatch alarm that:
- Triggers when free storage space drops below 2GB
- Sends notification to an SNS topic called sandbox-alerts
- Uses a 5-minute evaluation period
- Provide the AWS CLI command to create it.

AI-Assisted Data Analysis

Behavioral Competencies

Beyond technical skills, employers value these traits when working with AI:

• Technological curiosity: Willingness to experiment with new AI tools and evaluate them honestly
• Critical thinking: Ability to assess AI output quality — don't blindly trust, always validate
• Adaptability: Comfort with rapidly evolving tools; today's best practice may change in 6 months
• Proactive improvement: Actively identifying where AI can automate repetitive tasks in your workflow

6. Customer-Facing Technical Communication

DevOps roles in product/mobility companies often include customer training sessions and being the primary technical contact. This requires translating complex infrastructure into understandable terms.

Technical Support Tiers

Conducting Customer Training Sessions

• Know your audience: Developers need API details; managers need architecture overviews
• Lead with value: Start with "what this gives you" before "how it works"
• Use live demos: Show the Console, dashboards, and deployment flows in real-time
• Prepare runbooks: Document step-by-step procedures in Confluence for self-service
• Follow up: Send a summary email, record the session, create a FAQ from questions asked

Key Takeaways

• Monitoring: Set up CloudWatch alarms, dashboards, and log groups before users report issues
• Troubleshooting: Follow a systematic framework: detect → triage → diagnose → resolve → postmortem
• Docker: Multi-stage builds keep images small; ECR stores them securely on AWS
• Toolchain: Jira for tracking, Confluence for docs, Git branching for code flow — connect them all
• AI: Use generative AI to accelerate scripting, troubleshooting, and documentation — but always validate output
• Communication: Translate technical complexity into business value for customers and stakeholders