Data Science Model deployments and Cloud Computing on GCP

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1 - Course Introduction and prerequisites

1 - Course Introduction and Section Walkthrough.mp4

02:52

2 - Course Prerequisites.mp4

01:16

3 - Course Material Github Repo.html

2 - Modern Day Cloud Concepts

4 - Introduction.mp4

00:53

5 - Scalability Horizontal vs Vertical Scaling.mp4

06:33

6 - Serverless Vs Servers and Containerization.mp4

10:09

7 - Microservice Architecture.mp4

06:02

8 - Event Driven Architecture.mp4

05:35

3 - Get Started with Google Cloud

9 - Setup GCP Trial Account.mp4

03:35

10 - Gcloud CLI Setup.mp4

04:23

11 - Get comfortable with basics of gcloud cli.mp4

08:22

12 - gsutil and bash command basics.mp4

08:23

4 - Cloud Run Serverless and containerized applications

13 - Section Introduction.mp4

00:36

14 - Introduction to Dockers.mp4

02:17

15 - Lab Install Docker Engine.mp4

02:15

16 - Lab Run Docker locally.mp4

07:31

17 - Lab Run and ship applications using the container registry.mp4

11:59

18 - Introduction to Cloud Run.mp4

01:43

19 - LabDeploy python application to Cloud run.mp4

10:12

20 - Cloud Run Application Scalability parameters.mp4

06:20

21 - Introduction to Cloud Build.mp4

02:21

22 - Lab Python application deployment using cloud build.mp4

07:40

23 - LabContinuous Deployment using cloud build and github.mp4

09:44

5 - Google App Engine For Serverless applications

24 - Introduction to App Engine.mp4

01:27

25 - App Engine Different Environments.mp4

01:04

26 - LabDeploy Python application to App Engine Part 1.mp4

05:02

27 - LabDeploy Python application to App Engine Part 2.mp4

05:20

28 - LabTraffic splitting in App Engine.mp4

03:52

29 - LabDeploy pythonbigquery application.mp4

06:57

30 - What is Caching and the usecases.mp4

03:41

31 - LabImplement Caching mechanism in python application Part 1.mp4

09:39

32 - LabImplement Caching mechanism in python application Part 2.mp4

03:31

33 - LabAssignment Implement Caching.mp4

02:18

34 - LabPython App deployment in flexible environment.mp4

04:49

35 - Lab Scalability and instance types in App Engine.mp4

10:13

6 - Cloud Functions Serverless and event driven applications

36 - Introduction.mp4

04:03

37 - LabDeploy python application using cloud storage triggers.mp4

10:46

38 - LabDeploy python application using pubsub triggers.mp4

05:05

39 - LabDeploy python application using http triggers.mp4

02:52

40 - Introduction to Cloud Datastore.mp4

02:32

41 - Overview Product wishlist usecase.mp4

02:10

42 - LabUsecase deployment part1.mp4

10:03

43 - LabUsecase deployment part2.mp4

05:24

7 - Data Science Models with Google App Engine

44 - Introduction to ML Model Lifecycle.mp4

03:39

45 - Overview Problem Statement.mp4

01:58

46 - LabDeploy Training Code to App Engine.mp4

10:22

47 - LabDeploy Model Serving Code to App Engine.mp4

05:34

48 - OverviewNew Use Case.mp4

01:45

49 - LabData Validation using App Engine.mp4

05:21

50 - LabWorkflow Template introduction.mp4

04:55

51 - LabFinal Solution Deployment using workflow and app engine.mp4

12:24

8 - Dataproc Serverless Pyspark

52 - Introduction.mp4

02:28

53 - PySpark Serverless Autoscaling Properties.mp4

02:15

54 - Persistent History Cluster.mp4

05:12

55 - Lab Develop and Submit Pyspark Job.mp4

06:09

56 - LabMonitoring and Spark UI.mp4

03:25

57 - Introduction to Airflow.mp4

04:13

58 - Lab Airflow with Serverless pyspark.mp4

12:12

59 - Wrap Up.mp4

01:49

9 - Vertex AI Machine Learning Framework

60 - Introduction.mp4

02:12

61 - Overview VertexAI UI.mp4

01:53

62 - LabCustom Model training using Web Console.mp4

13:13

63 - LabCustom Model training using SDK and Model Registries.mp4

06:57

64 - Lab Model Endpoint Deployment.mp4

02:58

65 - Lab Model Training Flow using Python SDK.mp4

03:21

66 - Lab Model Deployment Flow using Python SDK.mp4

12:56

67 - LabModel Serving using Endpoint with Python SDK.mp4

05:41

68 - Introduction to Kubeflow.mp4

04:02

69 - LabCode Walkthrough using Kubeflow and Python.mp4

08:49

70 - LabPipeline Execution in Kubeflow.mp4

04:47

71 - LabFinal Pipeline Visualization using Vertex UI and Walkthrough.mp4

02:22

72 - LabAdd Model Evaluation Step in Kubeflow before deployment.mp4

06:09

73 - Lab Reusing configuration files for pipeline execution and training.mp4

05:29

74 - Lab Assignment Usecase Fetch data from BigQuery.mp4

01:32

75 - Wrap Up.mp4

01:05

10 - Cloud Scheduler and Application Monitoring

76 - Introduction to Cloud Scheduler.mp4

00:54

77 - LabCloud Scheduler in action.mp4

05:30

78 - Lab Setup Alerting for Google App Engine Applications.mp4

08:25

79 - Lab Setup Alerting for Cloud Run Applications.mp4

06:25

80 - Lab Assignment Setup Alerting for Cloud Function Applications.mp4

02:22

Description

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

What You'll 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 is this for?

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

More details

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 with Kubeflow and includging Serverless Pyspark using Dataproc , App Engine and Cloud Run .
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 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 courseÂ across a variety of different services .
Who this course is for:
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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I am a Business oriented Data Architect with a vast experience in the field of Software Development,Distributed processing and data engineering on cloud . I have worked on different cloud platforms such as AWS & GCP and also with on-prem hadoop clusters. I also give seminars on Distributed processing using Spark , real time streaming and analytics and best practices for ETL and data governance.I am also a passionate coder ,love writing and building optimal data pipelines for robust data processing and streaming solutions .

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