Serverless Data Architecture & Containers On Google Cloud

What you'll learn
Cloud computing using serverless data components
Containerization of python based applications
Microservice and Event driven architecture
Deploying production level machine learning workflows on cloud
Requirements
Must have a fair idea of how cloud works and past experience in basic programming using python and sql
Description
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 and lab sessions covering Serverless Pyspark using Dataproc .Are you interested in learning & deploying applications at scale using Google Cloud platform ?Do you lack the hands on exposure when 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 FunctionsCloud 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 .
Overview
Section 1: Course Introduction and pre-requisites
Lecture 1 Course Introduction and Section Walkthrough
Lecture 2 Course Pre-requisites
Lecture 3 Course Material Github Repo
Section 2: Modern Day Cloud Concepts
Lecture 4 Introduction
Lecture 5 Scalability - Horizontal vs Vertical Scaling
Lecture 6 Serverless Vs Servers and Containerization
Lecture 7 Microservice Architecture
Lecture 8 Event Driven Architecture
Section 3: Get Started with Google Cloud
Lecture 9 Setup GCP Trial Account
Lecture 10 Gcloud CLI Setup
Lecture 11 Get comfortable with basics of gcloud cli
Lecture 12 gsutil and bash command basics
Section 4: Cloud Run - Serverless and containerized applications
Lecture 13 Section Introduction
Lecture 14 Introduction to Dockers
Lecture 15 Lab - Install Docker Engine
Lecture 16 Lab - Run Docker locally
Lecture 17 Lab - Run and ship applications using the container registry
Lecture 18 Introduction to Cloud Run
Lecture 19 Lab-Deploy python application to Cloud run
Lecture 20 Cloud Run Application Scalability parameters
Lecture 21 Introduction to Cloud Build
Lecture 22 Lab- Python application deployment using cloud build
Lecture 23 Lab-Continuous Deployment using cloud build and github
Section 5: Google App Engine - For Serverless applications
Lecture 24 Introduction to App Engine
Lecture 25 App Engine - Different Environments
Lecture 26 Lab-Deploy Python application to App Engine - Part 1
Lecture 27 Lab-Deploy Python application to App Engine - Part 2
Lecture 28 Lab-Traffic splitting in App Engine
Lecture 29 Lab-Deploy python-bigquery application
Lecture 30 What is Caching and the use-cases ?
Lecture 31 Lab-Implement Caching mechanism in python application - Part 1
Lecture 32 Lab-Implement Caching mechanism in python application - Part 2
Lecture 33 Lab-Assignment Implement Caching
Lecture 34 Lab-Python App deployment in flexible environment
Lecture 35 Lab- Scalability and instance types in App Engine
Section 6: Cloud Functions - Serverless and event driven applications
Lecture 36 Introduction
Lecture 37 Lab-Deploy python application using cloud storage triggers
Lecture 38 Lab-Deploy python application using pub-sub triggers
Lecture 39 Lab-Deploy python application usinghttp triggers
Lecture 40 Introduction to Cloud Datastore
Lecture 41 Overview Product wishlist use-case
Lecture 42 Lab-Use-case deployment part-1
Lecture 43 Lab-Use-case deployment part-2
Section 7: Data Science Models with Google App Engine
Lecture 44 Introduction to ML Model Lifecycle
Lecture 45 Overview - Problem Statement
Lecture 46 Lab-Deploy Training Code to App Engine
Lecture 47 Lab-Deploy Model Serving Code to App Engine
Lecture 48 Overview-New Use Case
Lecture 49 Lab-Data Validation using App Engine
Lecture 50 Lab-Workflow Template introduction
Lecture 51 Lab-Final Solution Deployment using workflow and app engine
Section 8: Dataproc Serverless Pyspark
Lecture 52 Introduction
Lecture 53 PySpark Serverless Autoscaling Properties
Lecture 54 Persistent History Cluster
Lecture 55 Lab - Develop and Submit Pyspark Job
Lecture 56 Lab-Monitoring and Spark UI
Lecture 57 Introduction to Airflow
Lecture 58 Lab- Airflow with Serverless pyspark
Lecture 59 Wrap Up
Section 9: Vertex AI - Machine Learning Framework
Lecture 60 Introduction
Lecture 61 Overview - VertexAI UI
Lecture 62 Lab-Custom Model training using Web Console
Lecture 63 Lab-Custom Model training using SDK and Model Registries
Lecture 64 Lab- Model Endpoint Deployment
Lecture 65 Lab- Model Training Flow using Python SDK
Lecture 66 Lab - Model Deployment Flow using Python SDK
Lecture 67 Lab-Model Serving using Endpoint with Python SDK
Lecture 68 Introduction to Kubeflow
Lecture 69 Lab-Code Walkthrough using Kubeflow and Python
Lecture 70 Lab-Pipeline Execution in Kubeflow
Lecture 71 Lab-Final Pipeline Visualization using Vertex UI and Walkthrough
Lecture 72 Lab-Add Model Evaluation Step in Kubeflow before deployment
Lecture 73 Lab- Reusing configuration files for pipeline execution and training
Lecture 74 Lab - Assignment Use-case - Fetch data from BigQuery
Lecture 75 Wrap Up
Section 10: Cloud Scheduler and Application Monitoring
Lecture 76 Introduction to Cloud Scheduler
Lecture 77 Lab-Cloud Scheduler in action
Lecture 78 Lab - Setup Alerting for Google App Engine Applications
Lecture 79 Lab - Setup Alerting for Cloud Run Applications
Lecture 80 Lab Assignment - Setup Alerting for Cloud Function Applications
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


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