Google Cloud Platform (GCP) basics in its own words, tutorials and documentations
Google Cloud Platform (GCP) Overview
Google Cloud Platform GCP in Google’s Own Words
“The Google Cloud Platform (GCP) is a suite of cloud services hosted on Google’s infrastructure. From computing and storage, to data analytics, machine learning, and networking, GCP offers a wide variety of services and APIs that can be integrated with any cloud-computing application or project — be it personal or enterprise-grade.”
If we write a value proposition for Google Cloud Platform:
For customers big and small, from Fortune 500 to startups, who wants to host servers, infrastructure, software and data in the cloud, Google provides the Google Cloud Platform(GCP) that offers end-to-end hosted, scalable cloud platform, unlike competitors like Amazon Web Service and Microsoft Azure, Google offers integrated, easy-to-use, expert-supported with state-of-the-art documentation, GPU-enabled services at the operation scale of Google, can easily support the launch of a game like Pokemon GO, a global viral and massively compute intensive AR game.
Google Cloud Platform Key Features
- On-demand services: No human intervention needed to get resources
- Broad network access: access from anywhere
- Resource pooling: Provider shares resources to customers
- Rapid elasticity: Get more resources quickly as needed
- Measured service: pay what you consume
Available Command Line Tools
- gcloud toolkit
- Supports shell run commands like touch, nano, and cat to create, edit, and output the content of files.
- Use SSH to remote access Google Console in browser
- Use $sudo for root access
- Switch user to root access using $sudo su
- $whoami will now return sudo
Advanced Options on GCP Dashboard
- Compute: houses a variety of machine types that support any type of workload. The different computing options let you decide how involved you want to be with operational details and infrastructure amongst other things.
- Storage: data storage and database options for structured or unstructured, relational or non relational data.
- Networking: services that balance application traffic and provision security rules amongst other things.
- Stackdriver: a suite of cross-cloud logging, monitoring, trace, and other service reliability tools.
- Tools: services for developers managing deployments and application build pipelines.
- Big Data: services that allow you to process and analyze large datasets.
Artificial Intelligence: a suite of APIs that run specific artificial intelligence and machine learning tasks on the Google Cloud platform.
GCP Tutorials and Certification on Coursera
Great selection of courses, but expensive though.
- https://www.coursera.org/learn/data-insights-gcp-apply-ml
- https://www.coursera.org/learn/gcp-big-data-ml-fundamentals
- https://www.coursera.org/learn/gcp-fundamentals
- https://www.coursera.org/learn/google-machine-learning
Free GCP podcast by Googlers — https://www.gcppodcast.com/
Curriculum Available on Coursera
- Explore a large data using Datalab and BigQuery
- Export data for machine learning using Cloud Dataflow
- Develop a machine learning model in Tensorflow
- Train a machine learning model at scale on Cloud ML Engine
- Deploy the trained ML model as a microservice
- Invoke the trained model from an AppEngine web application
Machine Learning on GCP
GCP offers Machine Learning engines, including Tensorflow (its branded machine learning, deep learning framework), ML APIs and even TPU like GPU in the cloud optimized for Tensorflow. “Google Cloud Machine Learning (ML) Engine is a managed service that enables developers and data scientists to build and bring superior machine learning models to production. Cloud ML Engine offers training and prediction services, which can be used together or individually. “
- https://cloud.google.com/ml-engine/
- https://cloud.google.com/ml-engine/docs/tensorflow/using-gpus
- https://cloud.google.com/compute/docs/gpus/
First Google Cloud’s official certification specialization.
Google Cloud Certification specialization tracks
- Associate Cloud Engineer
- Professional cloud architect
- Professional cloud network engineer
- Professional cloud security engineer
- Professional cloud developer
- Professional cloud data engineer
- G Suite
Google Cloud Terminology, Glossary
- Cloud
- Virtual Machine
- Jenkins : “Jenkins is a free and open source automation server.” Use to schedule jobs.
- YAML : configuration file
- SSH : secure command line connection to server (virtual machines)
Cost Saving Options Quotas and Limits
Google Cloud Cost Management
For example, the google natural language API, you can set the following limits.
- requests per day
- requests per minute
- request per minute per user
Data Science on Google Cloud
- Data Studio : easy drag and drop, pivot, data visualization. Create dashboard in minutes like COVID dashboard by Johns Hopkins.
Google Cloud Learning Paths
- Data Analyst learning path: “A Data Analyst gathers and analyzes data to identify trends and develops valuable insights to help solve problems.”
Data Engineer learning path : “A Data Engineer designs and builds systems that collect and transform the data used to inform business decisions.” - Database Engineer learning path: “A Database Engineer designs, creates, manages, migrates, and troubleshoots databases used by applications to store and retrieve data.”
- “Data Engineering, Big Data, and Machine Learning on GCP Specialization”
Source: Google cloud site
Other Interesting Facts about GCP
- Getting started with data science on Google Cloud for free. Data science google cloud [public]
https://ml.learn-to-code.co/skillView.html?skill=8IrRlE8I6d0Jyr3iDdAx - AWS is probably the main competitor of GCP.
- Google Cloud Platform conference is called Google NEXT. Google Developer conference is called Google IO.
- Interesting APIs on GCP include Google Object Detection API, Google Translate, Google Natural Language Processing, AutoML
- Google Cloud blog is the best place to learn about current research and product development at GCP — https://cloud.google.com/blog/
- Major competitor: AWS EC2 which still dominates the market.
