Popoola Daniel's GADS Practise Project

Google Cloud Practise project

The cloud challenge is divided into 2 parts:

For the first challenge, I completed 12 Hands-on labs on Qwiklabs, I have uploaded proof of completion of each and saved them in the folder "GADS Practise Project/Screenshots".

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For the second challenge, I translated 2 labs from console instructions to 100% command line instructions, and I saved them inside the folder "GADS Practise Project/Translations"

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The labs I completed and their objectives are:

From the Associate Cloud Engineer: Learning Phase 1 Main Track Channel (2020):

1. Google Cloud Fundamentals: Getting Started with App Engine

Objectives:

* Initialize App Engine.
* Preview an App Engine application running locally in Cloud Shell.
* Deploy an App Engine application, so that others can reach it.
* Disable an App Engine application, when you no longer want it to be visible.

2. Google Cloud Fundamentals: Getting Started with Compute Engine

Objectives:

* Create a Compute Engine virtual machine using the Google Cloud Platform (GCP) Console.
* Create a Compute Engine virtual machine using the gcloud command-line interface.
* Connect between the two instances.

3. Google Cloud Fundamentals: Getting Started with Cloud Marketplace

Objectives:

* Launching a solution using Cloud Marketplace

4. Google Cloud Fundamentals: Getting Started with GKE

Objectives:

* Provision a Kubernetes cluster using Kubernetes Engine.
* Deploy and manage Docker containers using kubectl.

5. HTTP Load Balancer with AutoScaling

Objectives:

* Create a health check firewall rule
* Create a NAT configuration using Cloud Router
* Create a custom image for a web server
* Create an instance template based on the custom image
* Create two managed instance groups
* Configure an HTTP load balancer with IPv4 and IPv6
* Stress test an HTTP load balancer

6. Virtual Private Networks(VPN)

Objectives:

* Create VPN gateways in each network
* Create VPN tunnels between the gateways
* Verify VPN connectivity

From the Phase 1 Deepdive Channel:

7. AK8S-04 Creating a GKE Cluster via Cloud Shell

Objectives:

* Use kubectl to build and manipulate GKE clusters
* Use kubectl and configuration files to deploy Pods
* Use Container Registry to store and deploy containers

From the Phase 2 Maintrack Channel:

8. Classify Images with Pre-built ML Models using Cloud Vision API and AutoML

Objectives:

*Upload a labeled dataset to Google Cloud Storage and connect it to AutoML Vision with a CSV label file.
* Train a model with AutoML Vision and evaluate its accuracy.
* Generate predictions on your trained model.

9. Create a Streaming Data Pipeline for a Real-Time Dashboard with Cloud Dataflow

Objectives:

* You will build a streaming data pipeline to capture taxi revenue, passenger count, ride status, and much more and 
visualize the results in a management dashboard.
* Create a Cloud Pub/Sub Topic
* Create a BigQuery dataset
* Create a Cloud Storage Bucket
* Set up a Cloud Dataflow Pipeline
* Analyze the Taxi Data Using BigQuery
* Perform aggregations on the stream for reporting
* Create a Real-Time Dashboard

10. Predict Visitor Purchases with a Classification Model with BigQuery ML

Objectives:

* Use BigQuery to find public datasets
* Query and explore the ecommerce dataset
* Create a training and evaluation dataset to be used for batch prediction
* Create a classification (logistic regression) model in BQML
* Evaluate the performance of your machine learning model
* Predict and rank the probability that a visitor will make a purchase

11. Recommend Products using ML with Cloud SQL and Dataproc

Objectives:

* Create Cloud SQL instance
* Create tables
* Stage Data in Google Cloud Storage
* Loading Data from Google Cloud Storage into Cloud SQL tables
* Generating housing recommendations with Machine Learning using Cloud Dataproc

From the Phase 2 Deepdive Channel:

12. Running Apache Spark jobs on Cloud Dataproc

Objectives

* Migrate existing Spark jobs to Cloud Dataproc
* Modify Spark jobs to use Cloud Storage instead of HDFS
* Optimize Spark jobs to run on Job specific clusters

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The labs I translated to 100% command line instructions are:

1. Google Cloud Fundamentals: Getting Started with App Engine

2. Google Cloud Fundamentals: Getting Started with Compute Engine