Google Cloud for Machine Learning 2020 Master Course
- 4.1
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Brief Introduction
Master Google Cloud with comprehensive coverage of tools like: Serverless, BigQuery, Compute Engine and Cloud FunctionsDescription
Cloud Computing is one of the highest paying and most demanding job category in technology. Most businesses in recent years have started using cloud services like database, networking, servers, analytics, and intelligence for their business needs. Using cloud services not only helps with smart usage of infrastructure but also minimizes operational costs.
Google Cloud is quickly gaining market adoption due to some of its offerings in the Data Analytics and Serverless domain. Looking at the future, Google Cloud would be an excellent choice.
This course aims at covering a lot of the most used Google cloud products.
App Engine: App Engine is one of Google Cloud's serverless platforms. App Engine enables you to create infinitely scalable applications and deployments. In this section, you will be able to, Create an app engine project on Google Cloud. Host a static website on App Engine. Create an API using App Engine.
Cloud Functions: Cloud Functions is Google Cloud's biggest offering in the abstracted serverless environment. Cloud functions make the deployment of simple and repeated tasks easier. In this section, you will be able to create a cloud function using Python and Javascript. You will also be able to use cloud functions as a middleware for App Engine and perform event driven tasks.
Cloud Compute Engine: Cloud compute engine is Google Cloud's offering for the Virtual Machine space. You can create a Virtual machine with complete custom hardware and software. In this section, we will be creating a Virtual Machine on Google Cloud and create a CPU intensive program to benchmark the Virtual Machine.
Cloud Firestore: Cloud Firestore is a fully managed NoSQL database platform offered by Google Cloud and Firebase. We will be performing CRUD operations with Firebase and use it with App Engine.
Cloud BigQuery: Cloud BigQuery is Google Cloud's offering for big data related workloads. In this section, we will be creating a custom dataset using Python, we will host this dataset on Cloud BigQuery and then perform SQL queries on the database
Overall, this course aims at providing a holistic understanding of the software development cycle on Google Cloud. Most of the essential steps from writing code to staging deployments using Git and GitHub are covered in this course.
Requirements
- Requirements
- There are no pre-requisites for the course
- No software is required for this course apart from a web browser
Knowledge
- Learn the Essentials of Google Cloud
- Set up web apps using App Engine
- Create Virtual Machines on Google Cloud
- Deploy scalable applications on Google Cloud
- Create Cloud Functions using Python and Javascript
- Create event driven cloud functions
- Create a custom Compute Engine cluster
- Work with Firebase Database
- Understand the difference between SQL vs NoSQL architecture
- Create a fully managed Database offering
- Create Cloud BigQuery tables for storing Big Data
- Understand the SQL database query structure
- Understand the fundamentals of Git and GitHub
- Introduction to Machine Learning
- Introduction to Neural Networks
- Build and Deploy Machine Learning models on Google Cloud
Vinay Phadnis
4.1 Raiting
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