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GCP – Serverless Computing & AI Platform for Data Science

IT & Software

Rating4.7
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GCP – Serverless Computing & AI Platform for Data Science, available at $59.99, has an average rating of 4.7, with 80 lectures, based on 56 reviews, and has 723 subscribers.

You will learn about Deploy serverless applications using Google App Engine , Cloud Functions & Cloud Run Learn how to use datastore (NoSql Database) in realistic use-cases Microservice and Event driven architecture with practical examples Deploying production level machine learning workflows on cloud Use Kubeflow for Machine learning orchestration using Python Deploy Serverless Pyspark Jobs to Dataproc Serverless and schedule them using Airflow/Composer This course is ideal for individuals who are Aspiring data scientists and machine learning engineers or Data engineers and architects or Anyone who has a decent exposure in IT and wants to start their cloud journey It is particularly useful for Aspiring data scientists and machine learning engineers or Data engineers and architects or Anyone who has a decent exposure in IT and wants to start their cloud journey.

Enroll now: GCP – Serverless Computing & AI Platform for Data Science

Summary

Title: GCP – Serverless Computing & AI Platform for Data Science

Price: $59.99

Average Rating: 4.7

Number of Lectures: 80

Number of Published Lectures: 80

Number of Curriculum Items: 80

Number of Published Curriculum Objects: 80

Original Price: $64.99

Quality Status: approved

Status: Live

What You Will Learn

  • Deploy serverless applications using Google App Engine , Cloud Functions & Cloud Run
  • Learn how to use datastore (NoSql Database) in realistic use-cases
  • Microservice and Event driven architecture with practical examples
  • Deploying production level machine learning workflows on cloud
  • Use Kubeflow for Machine learning orchestration using Python
  • Deploy Serverless Pyspark Jobs to Dataproc Serverless and schedule them using Airflow/Composer

Who Should Attend

  • Aspiring data scientists and machine learning engineers
  • Data engineers and architects
  • Anyone who has a decent exposure in IT and wants to start their cloud journey

Target Audiences

  • Aspiring data scientists and machine learning engineers
  • Data engineers and architects
  • Anyone who has a decent exposure in IT and wants to start their cloud journey

Google Cloud platform is one of the fastest growing cloud providers right now . This course covers all the major serverless components on GCP including a detailed implementation of Machine learning pipelines using Vertex AI with Kubeflow and includging Serverless Pyspark using Dataproc , App Engine and Cloud Run .

Are you interested in learning & deployingapplications at scale using Google Cloud platform ?

Do you lack the hands on exposurewhen it comes to deploying applications and seeing them in action?

If you answered “yes” to the above questions,then this course is for you .

You will also learn what are micro-service and event driven architectures are with real world use-case implementations .

This course is for anyone who wants to get a hands-on exposure in using the below services :

  • Cloud Functions

  • Cloud Run

  • Google App Engine

  • Vertex AI for custom model training and development

  • Kubeflow for workflow orchestration

  • Dataproc Serverless for Pyspark batch jobs

This course expects and assumes the students to have :

  • A tech background with basic fundamentals

  • Basic exposure to programming languages like Python & Sql

  • Fair idea of how cloud works

  • Have the right attitude and patience for self-learning 🙂

You will learn how to design and deploy applications written in Python which is the scripting language used in this course  across a variety of different services .

Course Curriculum

Chapter 1: Course Introduction and pre-requisites

Lecture 1: Course Introduction and Section Walkthrough

Lecture 2: Course Pre-requisites

Lecture 3: Course Material Github Repo

Chapter 2: Modern Day Cloud Concepts

Lecture 1: Introduction

Lecture 2: Scalability – Horizontal vs Vertical Scaling

Lecture 3: Serverless Vs Servers and Containerization

Lecture 4: Microservice Architecture

Lecture 5: Event Driven Architecture

Chapter 3: Get Started with Google Cloud

Lecture 1: Setup GCP Trial Account

Lecture 2: Gcloud CLI Setup

Lecture 3: Get comfortable with basics of gcloud cli

Lecture 4: gsutil and bash command basics

Chapter 4: Cloud Run – Serverless and containerized applications

Lecture 1: Section Introduction

Lecture 2: Introduction to Dockers

Lecture 3: Lab – Install Docker Engine

Lecture 4: Lab – Run Docker locally

Lecture 5: Lab – Run and ship applications using the container registry

Lecture 6: Introduction to Cloud Run

Lecture 7: Lab-Deploy python application to Cloud run

Lecture 8: Cloud Run Application Scalability parameters

Lecture 9: Introduction to Cloud Build

Lecture 10: Lab- Python application deployment using cloud build

Lecture 11: Lab-Continuous Deployment using cloud build and github

Chapter 5: Google App Engine – For Serverless applications

Lecture 1: Introduction to App Engine

Lecture 2: App Engine – Different Environments

Lecture 3: Lab-Deploy Python application to App Engine – Part 1

Lecture 4: Lab-Deploy Python application to App Engine – Part 2

Lecture 5: Lab-Traffic splitting in App Engine

Lecture 6: Lab-Deploy python-bigquery application

Lecture 7: What is Caching and the use-cases ?

Lecture 8: Lab-Implement Caching mechanism in python application – Part 1

Lecture 9: Lab-Implement Caching mechanism in python application – Part 2

Lecture 10: Lab-Assignment Implement Caching

Lecture 11: Lab-Python App deployment in flexible environment

Lecture 12: Lab- Scalability and instance types in App Engine

Chapter 6: Cloud Functions – Serverless and event driven applications

Lecture 1: Introduction

Lecture 2: Lab-Deploy python application using cloud storage triggers

Lecture 3: Lab-Deploy python application using pub-sub triggers

Lecture 4: Lab-Deploy python application using http triggers

Lecture 5: Introduction to Cloud Datastore

Lecture 6: Overview Product wishlist use-case

Lecture 7: Lab-Use-case deployment part-1

Lecture 8: Lab-Use-case deployment part-2

Chapter 7: Data Science Models with Google App Engine

Lecture 1: Introduction to ML Model Lifecycle

Lecture 2: Overview – Problem Statement

Lecture 3: Lab-Deploy Training Code to App Engine

Lecture 4: Lab-Deploy Model Serving Code to App Engine

Lecture 5: Overview-New Use Case

Lecture 6: Lab-Data Validation using App Engine

Lecture 7: Lab-Workflow Template introduction

Lecture 8: Lab-Final Solution Deployment using workflow and app engine

Chapter 8: Dataproc Serverless Pyspark

Lecture 1: Introduction

Lecture 2: PySpark Serverless Autoscaling Properties

Lecture 3: Persistent History Cluster

Lecture 4: Lab – Develop and Submit Pyspark Job

Lecture 5: Lab-Monitoring and Spark UI

Lecture 6: Introduction to Airflow

Lecture 7: Lab- Airflow with Serverless pyspark

Lecture 8: Wrap Up

Chapter 9: Vertex AI – Machine Learning Framework

Lecture 1: Introduction

Lecture 2: Overview – VertexAI UI

Lecture 3: Lab-Custom Model training using Web Console

Lecture 4: Lab-Custom Model training using SDK and Model Registries

Lecture 5: Lab- Model Endpoint Deployment

Lecture 6: Lab- Model Training Flow using Python SDK

Lecture 7: Lab – Model Deployment Flow using Python SDK

Lecture 8: Lab-Model Serving using Endpoint with Python SDK

Lecture 9: Introduction to Kubeflow

Lecture 10: Lab-Code Walkthrough using Kubeflow and Python

Lecture 11: Lab-Pipeline Execution in Kubeflow

Lecture 12: Lab-Final Pipeline Visualization using Vertex UI and Walkthrough

Lecture 13: Lab-Add Model Evaluation Step in Kubeflow before deployment

Lecture 14: Lab- Reusing configuration files for pipeline execution and training

Lecture 15: Lab – Assignment Use-case – Fetch data from BigQuery

Lecture 16: Wrap Up

Chapter 10: Cloud Scheduler and Application Monitoring

Lecture 1: Introduction to Cloud Scheduler

Lecture 2: Lab-Cloud Scheduler in action

Lecture 3: Lab – Setup Alerting for Google App Engine Applications

Lecture 4: Lab – Setup Alerting for Cloud Run Applications

Lecture 5: Lab Assignment – Setup Alerting for Cloud Function Applications

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 4 votes
  • 3 stars: 3 votes
  • 4 stars: 19 votes
  • 5 stars: 30 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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