Day 5: Monitoring, Logging, and CI/CD

Today's Objective

Cloud Monitoring, Cloud Logging, Error Reporting, and Cloud Deploy — the observability and delivery pipeline that keeps production healthy.

Pre-built AI APIs

GCP offers AI APIs that require zero ML knowledge — just HTTP calls. They're trained by Google and ready to use.

Terminal

pip install google-cloud-vision google-cloud-language google-cloud-aiplatform

Vision API

from google.cloud import vision

client = vision.ImageAnnotatorClient()

with open('photo.jpg', 'rb') as f:
    content = f.read()

image = vision.Image(content=content)

# Detect labels
response = client.label_detection(image=image)
for label in response.label_annotations:
    print(f'{label.description}: {label.score:.2f}')

# Read text from image
response = client.text_detection(image=image)
print(response.text_annotations[0].description)

Gemini via Vertex AI

import vertexai
from vertexai.generative_models import GenerativeModel

vertexai.init(project='YOUR_PROJECT_ID', location='us-central1')

model = GenerativeModel('gemini-1.5-flash')

response = model.generate_content(
    'Explain cloud computing in one paragraph for a beginner.'
)
print(response.text)

# With a system prompt
model = GenerativeModel(
    'gemini-1.5-flash',
    system_instruction='You are a concise technical writer. Always use bullet points.'
)
response = model.generate_content('What is Kubernetes?')
print(response.text)

ℹ️

Vertex AI is GCP's unified ML platform. It hosts both Google's models (Gemini, Imagen) and lets you deploy your own. For most applications, the pre-built Gemini models via Vertex AI are all you need.

📝 Day 5 Exercise

Build an AI Data Pipeline

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Day 5 Summary

Vision API: labels, text (OCR), faces, objects — all from a single image upload.
Natural Language API: sentiment, entities, syntax — for text analysis without training a model.
Vertex AI hosts Gemini. The Python SDK mirrors the Gemini API syntax.
Pre-built APIs cost per call but require zero training data or ML expertise.

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Day 4: BigQuery for Analytics

Course Complete

Back to Google Cloud in 5 Days

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Course Complete

Completing all five days means having a solid working knowledge of Google Cloud Platform in 5 Days. The skills here translate directly to real projects. The next step is practice — pick a project and build something with what was learned.

Supporting Videos & Reading

Go deeper with these external references.

YouTube

Monitoring, Logging, and CI/CD — Video Walkthroughs Community tutorials and walkthroughs covering the concepts in this lesson.

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YouTube

Monitoring, Logging, and CI/CD Explained Deep-dive explanations and live-coding sessions from top educators.

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Official Docs

Official Documentation Primary reference documentation for the technologies covered in this lesson.

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GitHub

Open Source Examples Real-world codebases demonstrating the patterns taught in this lesson.

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Day 5 Checkpoint

Before moving on, verify you can answer these without looking:

What is the core concept introduced in this lesson, and why does it matter?
What are the two or three most common mistakes practitioners make with this topic?
Can you explain the key code pattern from this lesson to a colleague in plain language?
What would break first if you skipped the safeguards or best practices described here?
How does today's topic connect to what comes in Day the final lesson?

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