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Photo: Joe deSousa (CC0)
Spotlight
PAGE: https://alison.com/course/diploma-in-devops-engineering-kubernetes-docker-and-google-cloud
PROMISE: Stop guessing at silent Kubernetes loops by learning core container and GCP concepts.
WHO: Beginners in cloud infra who need a fast conceptual refresher on K8s and GCP.
WHYNOW: Your current silent failures are blocking quick fixes and demanding a systematic learning approach.
DESC: This is a project-based course, where the fundamentals, concepts and terminology used in Kubernetes, Docker and the Google Cloud platform is covered.
BEFORE: Silent failures require guessing at root causes
BEFORE: Console navigation feels like trial and error
BEFORE: Debugging sessions drag into late-night rabbit holes
AFTER: Failures are traced to specific container states
AFTER: Console navigation follows a clear logical map
AFTER: Debugging sessions use structured diagnostic steps
GAIN: Vocabulary to read CrashLoopBackOff logs accurately
GAIN: Ability to distinguish GCP infra issues from app bugs
GAIN: CPD-accredited certificate documenting foundational knowledge
STEP: Your pod enters CrashLoopBackOff and you lack the vocabulary to debug it. || Explain what Kubernetes does and its relationship to containers || Read pod states and logs with context, identifying if the failure is a container or orchestrator issue.
STEP: Builds succeed locally but fail in the cloud, leaving you stuck in a rebuild cycle. || Create a Google Cloud account and discuss different tools and menu features in the Google Cloud platform || Navigate the GCP console to check deployment configs and network settings directly.
STEP: You see a service outage but cannot tell if it is an app bug or infra fault. || Summarize the functions of Kubernetes, Docker and Google Cloud platforms || Map the failure to a specific layer, separating Docker image issues from Kubernetes orchestration errors.
NOTFOR: Teams needing advanced GKE autoscaling tuning immediately
NOTFOR: Developers wanting hands-on CLI practice over conceptual theory
FACT: Average duration: 5-6 Hours (Contains Video)
FACT: CPD-accredited certificate available
FACT: Beginner level
COVERS: Having completed this course you will be able to:
COVERS: Discuss the fundamental concepts and terminology of Microservices computing
COVERS: Summarize the functions of Kubernetes, Docker and Google Cloud platforms
COVERS: Create a Google Cloud account and discuss different tools and menu features in the Google Cloud platform
COVERS: Explain what Kubernetes does and its relationship to containers
COVERS: App Development
COVERS: PHP
COVERS: Docker
COVERS: DevOps
COVERS: MySQL
COVERS: Google Cloud
COVERS: Google Workspace
MODULE: Introduction to the Course and Microservices || Course Structure | Course Content | Microservices Computing
MODULE: Google Cloud and Docker || Google Cloud Account | Understanding Microservices | Understanding Containers | Google Cloud Platform | Introduction to Docker
MODULE: Virtual Box and Kubernetes || Installing the Virtual Box | Installing Docker Toolbox | Docker Hub | Introduction to Kubernetes
MODULE: Google Cloud Shell || Google Cloud Shell | Google Cloud Engines | Kubernetes Architecture | Creating a VM in Shell | Pulling Docker Images
MODULE: Containers and Images || Pulling Repositories | Creating Images Manually | Kubernetes Components | Creating a Cluster | Kubernetes Interface
MODULE: Deploying an App || App Deployment | Pushing Docker Images | Guestbook Project | Running the App | Redis Master Service
MODULE: Frontend Development || Worker Pods | Redis Slave Service | Developing the Frontend | Frontend PHP File | Project Testing and Deletion
MODULE: Course Review || Project Summary | Course Recap
What you are probably seeing
You find yourself up late at night investigating a production issue that shows no errors in your standard logs but users report total unresponsiveness. The dashboard looks healthy, yet traffic is dropping because the system is frozen in a retry loop without throwing exceptions.
You spend hours manually correlating trace IDs across services to find the root cause, only to realize that the data required to pinpoint the failure was never captured because your monitoring focused on metrics rather than user experience.
Why it happens
Modern distributed systems often handle errors by retrying or failing fast without logging critical context, leading to silent disconnects where the infrastructure appears stable while the user experience is broken. These failures hide in the gaps between microservices where standard alerts do not trigger.
Traditional observability tools rely on you defining what to look for, so unexpected loop states or slow dependencies often go undetected until they impact revenue. Without full-fidelity session data, you lack the visual and interaction context needed to distinguish a bug from a network hiccup quickly.
What changed recently
2026-10-07 · AI agents can’t fix infrastructure failures they can’t diagnose
VentureBeat is now mapping that gap for cloud infrastructure failures:
• 59% of U.S. enterprises cannot auto-identify root cause of AI workload failures.
• 66% of U.S. enterprises run AI infrastructure without reliable performance baselines.
Source: VentureBeat
2026-10-07 · Update on global technology issue
Delta is stabilizing systems for that gap:
• Delta systems are stabilizing after a limited global technology issue
• The issue affected data connectivity to some systems behind-the-scenes
• Delta teams are working to keep operations moving safely
Source: delta_news
2026-10-07 · The network monitoring tool shakeout of 2026 – Yahtzee style!
The tool shakeout forces a clearer path for that gap:
• Zabbix: free, open source SNMP and API monitoring.
• SolarWinds NPM: self-hosted for detailed data.
• Auvik: cloud-managed for multi-office networks.
Source: Spiceworks
2026-10-06 · Observability for AI-native systems: New SLIs beyond latency and error rate
InfoWorld outlines new SLIs for AI-native systems to fill that gap:
• Split latency into queue, retrieval, first token, and generation time
• Agent observability tools show 12 to 15 percent runtime overhead
Source: InfoWorld
2026-10-06 · Cloudflare Observability: $0.25/GB Takes on Datadog [2026]
Cloudflare is now tracing requests edge-to-origin:
• Trace requests in one view from edge to origin
• Query logs and traces via a unified SQL API
• Manage logs, alerts, and dashboards in one place
• Ingest 50 GB of data per cycle on paid plans
Source: shattered.io
Step by step
1. Audit your current logging configuration to ensure that retry logic and circuit breaker states are explicitly recorded with enough context to diagnose loops.
2. Integrate real user monitoring into your observability stack to capture full-fidelity sessions that reveal latency from the user’s perspective rather than just server-side metrics.
3. Evaluate observability platforms for built-in AI agents that can correlate disparate data points to recommend specific fixes in minutes rather than hours of manual tracing.
4. Test your infrastructure’s failure modes by simulating stuck loops and verify that your new monitoring setup detects the state change before it impacts customers.
5. Review your on-call procedures to prioritize actions recommended by your automated agents and reduce the cognitive load during late-night incidents.
Ways to go further
Start here: Diploma in DevOps Engineering – Kubernetes, Docker and Google Cloud For when you need to set up cloud logging to track distributed system failures. Open the free course on Alison ↗
Better fit: Basics of Kubernetes Using Docker For when you want to understand how Kubernetes and Docker logs work together. Open the free course on Alison ↗
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