GCP - Serverless Computing & AI Platform for Data Science
- Descrição
- Currículo
- FAQ
- Revisões
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 :
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Cloud Functions
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Cloud Run
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Google App Engine
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Vertex AI for custom model training and development
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Kubeflow for workflow orchestration
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Dataproc Serverless for Pyspark batch jobs
This course expects and assumes the students to have :
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A tech background with basic fundamentals
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Basic exposure to programming languages like Python & Sql
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Fair idea of how cloud works
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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 .
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13
Section Introduction -
14
Introduction to Dockers
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15
Lab - Install Docker Engine
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16
Lab - Run Docker locally
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17
Lab - Run and ship applications using the container registry
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18
Introduction to Cloud Run
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19
Lab-Deploy python application to Cloud run
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20
Cloud Run Application Scalability parameters
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21
Introduction to Cloud Build
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22
Lab- Python application deployment using cloud build
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23
Lab-Continuous Deployment using cloud build and github
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24
Introduction to App Engine
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25
App Engine - Different Environments
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26
Lab-Deploy Python application to App Engine - Part 1
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27
Lab-Deploy Python application to App Engine - Part 2
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28
Lab-Traffic splitting in App Engine
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29
Lab-Deploy python-bigquery application
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30
What is Caching and the use-cases ?
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31
Lab-Implement Caching mechanism in python application - Part 1
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32
Lab-Implement Caching mechanism in python application - Part 2
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33
Lab-Assignment Implement Caching
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34
Lab-Python App deployment in flexible environment
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35
Lab- Scalability and instance types in App Engine
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36
Introduction
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37
Lab-Deploy python application using cloud storage triggers
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38
Lab-Deploy python application using pub-sub triggers
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39
Lab-Deploy python application using http triggers
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40
Introduction to Cloud Datastore
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41
Overview Product wishlist use-case
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42
Lab-Use-case deployment part-1
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43
Lab-Use-case deployment part-2
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44
Introduction to ML Model Lifecycle
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45
Overview - Problem Statement
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46
Lab-Deploy Training Code to App Engine
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47
Lab-Deploy Model Serving Code to App Engine
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48
Overview-New Use Case
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49
Lab-Data Validation using App Engine
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50
Lab-Workflow Template introduction
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51
Lab-Final Solution Deployment using workflow and app engine
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60
Introduction
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61
Overview - VertexAI UI
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62
Lab-Custom Model training using Web Console
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63
Lab-Custom Model training using SDK and Model Registries
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64
Lab- Model Endpoint Deployment
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65
Lab- Model Training Flow using Python SDK
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66
Lab - Model Deployment Flow using Python SDK
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67
Lab-Model Serving using Endpoint with Python SDK
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68
Introduction to Kubeflow
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69
Lab-Code Walkthrough using Kubeflow and Python
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70
Lab-Pipeline Execution in Kubeflow
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71
Lab-Final Pipeline Visualization using Vertex UI and Walkthrough
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72
Lab-Add Model Evaluation Step in Kubeflow before deployment
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73
Lab- Reusing configuration files for pipeline execution and training
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74
Lab - Assignment Use-case - Fetch data from BigQuery
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75
Wrap Up
