Data Science Model deployments and Cloud Computing on GCP

سرفصل های دوره

Learn to deploy and implement applications at scale using Kubelow, spark and serverless components on Google Cloud

1 - Course Introduction and prerequisites

1 - Course Introduction and Section Walkthrough

2 - Course Prerequisites

3 - Course Material Github Repo.html

2 - Modern Day Cloud Concepts

4 - Introduction

5 - Scalability Horizontal vs Vertical Scaling

6 - Serverless Vs Servers and Containerization

7 - Microservice Architecture

8 - Event Driven Architecture

3 - Get Started with Google Cloud

9 - Setup GCP Trial Account

10 - Gcloud CLI Setup

11 - Get comfortable with basics of gcloud cli

12 - gsutil and bash command basics

4 - Cloud Run Serverless and containerized applications

13 - Section Introduction

14 - Introduction to Dockers

15 - Lab Install Docker Engine

16 - Lab Run Docker locally

17 - Lab Run and ship applications using the container registry

18 - Introduction to Cloud Run

19 - LabDeploy python application to Cloud run

20 - Cloud Run Application Scalability parameters

21 - Introduction to Cloud Build

22 - Lab Python application deployment using cloud build

23 - LabContinuous Deployment using cloud build and github

5 - Google App Engine For Serverless applications

24 - Introduction to App Engine

25 - App Engine Different Environments

26 - LabDeploy Python application to App Engine Part 1

27 - LabDeploy Python application to App Engine Part 2

28 - LabTraffic splitting in App Engine

29 - LabDeploy pythonbigquery application

30 - What is Caching and the usecases

31 - LabImplement Caching mechanism in python application Part 1

32 - LabImplement Caching mechanism in python application Part 2

33 - LabAssignment Implement Caching

34 - LabPython App deployment in flexible environment

35 - Lab Scalability and instance types in App Engine

6 - Cloud Functions Serverless and event driven applications

36 - Introduction

37 - LabDeploy python application using cloud storage triggers

38 - LabDeploy python application using pubsub triggers

39 - LabDeploy python application using http triggers

40 - Introduction to Cloud Datastore

41 - Overview Product wishlist usecase

42 - LabUsecase deployment part1

43 - LabUsecase deployment part2

7 - Data Science Models with Google App Engine

44 - Introduction to ML Model Lifecycle

45 - Overview Problem Statement

46 - LabDeploy Training Code to App Engine

47 - LabDeploy Model Serving Code to App Engine

48 - OverviewNew Use Case

49 - LabData Validation using App Engine

50 - LabWorkflow Template introduction

51 - LabFinal Solution Deployment using workflow and app engine

8 - Dataproc Serverless Pyspark

52 - Introduction

53 - PySpark Serverless Autoscaling Properties

54 - Persistent History Cluster

55 - Lab Develop and Submit Pyspark Job

56 - LabMonitoring and Spark UI

57 - Introduction to Airflow

58 - Lab Airflow with Serverless pyspark

59 - Wrap Up

9 - Vertex AI Machine Learning Framework

60 - Introduction

61 - Overview VertexAI UI

62 - LabCustom Model training using Web Console

63 - LabCustom Model training using SDK and Model Registries

64 - Lab Model Endpoint Deployment

65 - Lab Model Training Flow using Python SDK

66 - Lab Model Deployment Flow using Python SDK

67 - LabModel Serving using Endpoint with Python SDK

68 - Introduction to Kubeflow

69 - LabCode Walkthrough using Kubeflow and Python

70 - LabPipeline Execution in Kubeflow

71 - LabFinal Pipeline Visualization using Vertex UI and Walkthrough

72 - LabAdd Model Evaluation Step in Kubeflow before deployment

73 - Lab Reusing configuration files for pipeline execution and training

74 - Lab Assignment Usecase Fetch data from BigQuery

75 - Wrap Up

10 - Cloud Scheduler and Application Monitoring

76 - Introduction to Cloud Scheduler

77 - LabCloud Scheduler in action

78 - Lab Setup Alerting for Google App Engine Applications

79 - Lab Setup Alerting for Cloud Run Applications

80 - Lab Assignment Setup Alerting for Cloud Function Applications

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تولید کننده: Udemy-Training