# GOOGLE公雲使用案例
## 大綱
1. Introduction(Global view)
2. How to choose ?
3. Practice
## Storage (冷資料/熱資料)
### Terms
1. Minimum storage duration
https://stackoverflow.com/questions/64442551/meaning-of-minimum-storage-duration-of-google-cloud-storage
3. Storage classes
https://cloud.google.com/storage/docs/storage-classes

| Hot | Hotter | Colder | Cold |
| -------- | -------- | -------- | ------- |
| Standard | Nearline | Coldline | Archive |
3. Location
https://cloud.google.com/storage/docs/locations
4. Google Storage-Pricing
https://cloud.google.com/storage/pricing#archival-pricing
### 中/英 圖解

Source:
1. [GCP SketchNotes](https://thecloudgirl.dev/)
- [Storage Options](https://thecloudgirl.dev/StorageOptions.html)

### 應用
| Region | Class | Data | Function | Mini. Duration | EX | Lab |
| ---------------------- | ---------------- | ---- | ------------------ | -------------- | -------------- | -------------- |
| Multi-Regional Storage | | Hot | 異地備份 | | billing | |
| | Nearline Storage | Cold | 資料備份 | 30 Days | 通聯資料 | |
| | Coldline Storage | Cold | 線上備份、救援回復 | 90 Days | 契約(稽核用) | [Cloud Storage: Qwik Start - Cloud Console](https://www.qwiklabs.com/focuses/1760?parent=catalog)|

### Reference
- [All you need to know about Cloud Storage](https://cloud.google.com/blog/topics/developers-practitioners/all-you-need-know-about-cloud-storage)

---

## Monitor / Alert( 如何監控服務 / 如何發送告警訊息 )

### How ➔ 如何監控服務
1. Create an uptime check
2. Monitor uptime checks

### How ➔ 如何發送告警訊息
- Notification channels

### Practice
[Quest: Monitor and Log with Google Cloud Operations Suite](https://www.qwiklabs.com/focuses/10599?parent=catalog)

### Document
https://cloud.google.com/monitoring

### Cloud Monitoring - 影片筆記
- **How** Cloud Monitoring can help you monitor,troubleshoot, and improve your infrastructure and applications performance?

- Google Cloud's <u>observability offerings</u>
- Provide visibility across your apps and infrastructure,regardless of whether your app is running in Google Cloud, On-Premises(本地), or in other clouds.

- Impossible all day looking at dashboards
- Provide **alerting** where you can **create policies**

- Allow you to define **custom metrics** unique to your application and business use cases

- Using the **Metrics Explorer** and **Monitoring Query Language**

- Analze these metrics on the fly (即時分析指標)
identify `correlations`, and easily add the `corresponding charts` to a dashboard.

- Consolid view (綜合視角)
- Health and Performance of apps and infrastructure

- Spot anomalies

- Using Google's data visualization tools, out-of-the-box dashboards

- How to access ?
- Access Cloud Monitoring on the **Google Cloud Console**
or using the **APIs**
- `GKE`: Monitoring is enabled by default for clusters
- `VM`: Install `agent` to get started
- `All GCP services`: Pre-configured to automatically collect metrics

#### Refence:
[YT-Cloud Monitoring in a minute](https://youtu.be/7BLV24noNGc)

---

## Recommend Service( AI / Machine Learning )

### Google Search's Related Result:
- [Recommendations AI](https://cloud.google.com/retail/recommendations-ai/docs/how-to?hl=zh-TW)
- PS: 有文件但還沒找到對應的Qwiklab來實作
### GCP x Qwiklabs - free
- The ML and AI track of the Google Cloud skills challenge

- 其他相關 Quest
- [(Quest) Advanced ML: ML Infrastructure](https://www.qwiklabs.com/quests/84?catalog_rank=%7B%22rank%22%3A1%2C%22num_filters%22%3A0%2C%22has_search%22%3Atrue%7D&search_id=12437681)
- [(Lab) Real Time Machine Learning with Google Cloud ML](https://www.qwiklabs.com/focuses/3393?parent=catalog)
- [(Lab) Awwvision: Cloud Vision API from a Kubernetes Cluster](https://www.qwiklabs.com/focuses/1241?parent=catalog)
- [(Lab) Distributed Machine Learning with Google Cloud ML](https://www.qwiklabs.com/focuses/3389?parent=catalog)
- [(Lab) Scikit-learn Model Serving with Online Prediction Using AI Platform](https://www.qwiklabs.com/focuses/1789?parent=catalog)

### Google 機器學習三大服務:
1. AutoML
2. Cloud ML Engine
3. ML API

### Reference:
1. https://blog.gcp.expert/google-cloud-automl-ml-engine-ml-api/

2. https://cloud.google.com/automl/

3. https://cloud.google.com/ml-engine/