Machine Learning on Google Cloud Vertex AI Hands on

Learn how to build & deploy ML/DL models using GCP components AutoML, AI Platform and Vertex AI
Machine Learning on Google Cloud Vertex AI Hands on
File Size :
2.96 GB
Total length :
6h 28m

Category

Instructor

Hemanth Kumar K

Language

Last update

11/2022

Ratings

4/5

Machine Learning on Google Cloud Vertex AI Hands on

What you’ll learn

Understanding of Google Cloud Platform
GCP Compute services
GCP Storage Services
GCP Database services
Identity & Access management (IAM) of GCP
GCP Analytics services
GCP AutoML – Model building & deployment for Tabular data
GCP AutoML – Model building & deployment for Image data
GCP AutoML – Model building & deployment for Text data
GCP AI Platform – Notebooks & model building
GCP AI Platform – model deployment
GCP AI Platform – Custom Predictors
GCP AI Platform – Jobs creation & submissions
GCP AI Platform – Creation and Running of pipelines using Docker Images
GCP Vertex AI – AutoML model training and deployment
GCP Vertex AI – Custom model training & deployment
GCP Vertex AI – Custom model with hyperparameter parameters tuning
GCP Vertex AI – Pipelines for training using AutoML component
GCP Vertex AI – Pipelines for training Custom Models
GCP Vertex AI – Feature Store

Machine Learning on Google Cloud Vertex AI Hands on

Requirements

No prerequisites on understanding of any cloud platform
Python & Basics of Data science

Description

Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP?If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.This course starts with providing an overview of the Google Cloud Platform, creating a GCP account, and providing a basic understanding of the platform. Before jumping into the AI services of GCP, this course introduces important services of GCP. Services include Compute, storage, database, IAM, and analytics, followed by a demo of one key component of these services. The last three sections of the course are dedicated to understanding and working on the AI services offered by GCP. You will work on model creation and deployment using AutoML for tabular, images, and text data. Getting predictions from the deployed model using APIs. In the AI platform section, you will work on model creation and deployment using AI Platform (both GUI and coding approach). Creation and submission of jobs and evaluation of the trained model. Pipeline creation using Kubeflow. And in the Vertex AI section, you will work on model creation using AutoML, custom model training, and deployment. Inclusion ofhyperparameter optimization step in the custom model. Kubeflow pipelines creation using AutoML & custom models. You will also work on the Feature store.

Overview

Section 1: Introduction

Lecture 1 Course Structure

Section 2: Basic understanding of gcp

Lecture 2 Footprint of gcp

Lecture 3 Cloud service model

Lecture 4 Broadview of gcp Services

Section 3: Getting started with gcp

Lecture 5 Gcp account creation

Lecture 6 Hierarchy in gcp & accessing resources

Section 4: Compute in gcp

Lecture 7 Introduction to compute

Lecture 8 Compute & Kubernetes

Lecture 9 Cloud functions & App engine

Lecture 10 Demo: VM creation

Lecture 11 Demo: VM access

Lecture 12 Summary of compute

Section 5: Storage & Database in gcp

Lecture 13 Introduction to storage & gcs

Lecture 14 Persistent disk & Filestore

Lecture 15 Demo on google cloud storage

Lecture 16 Introduction to database, cloud sql & bigtable

Lecture 17 Spanner, Memory store & Firebase

Lecture 18 Demo: Cloud SQL creation

Lecture 19 Demo: Cloud SQL access

Lecture 20 Summary of storage & database

Section 6: Identity & access management in gcp

Lecture 21 Introduction to Incident & access management (IAM)

Lecture 22 Demo: IAM

Lecture 23 Summary of IAM

Section 7: Analytics in gcp

Lecture 24 Introduction to analytics

Lecture 25 Pubsub & Dataproc

Lecture 26 Dataflow, Bigquery & Dataprep

Lecture 27 Demo: Bigquery 1

Lecture 28 Demo: Bigquery 2

Lecture 29 Demo: Bigquery 3

Lecture 30 Demo: Dataprep 1

Lecture 31 Demo: Dataprep 2

Lecture 32 Summary of analytics

Section 8: AutoML

Lecture 33 Introduction to AI services

Lecture 34 Introduction to Automl

Lecture 35 Automl Tables model training

Lecture 36 Automl Tables deployment & batch predictions

Lecture 37 Automl Tables online predictions

Lecture 38 Automl Vision model training

Lecture 39 Automl Vision model deployment

Lecture 40 Automl language model training

Lecture 41 Automl language model deployment

Lecture 42 Automl pre built models

Lecture 43 Summary of Automl

Section 9: AI platform

Lecture 44 Introduction to AI Platform

Lecture 45 AI Platform Notebooks

Lecture 46 AI Platform model deployment (console)

Lecture 47 AI Platform custom predictors 1

Lecture 48 AI Platform custom predictors 2

Lecture 49 Introduction to jobs on AI Platform Jobs

Lecture 50 AI Platform Jobs creation & submissions

Lecture 51 AI Platform Jobs evaluation & deployment

Lecture 52 Introduction to pipelines on AI Platform

Lecture 53 AI Platform pipeline docker image creation

Lecture 54 AI Platform pipeline configure code walk

Lecture 55 AI Platform pipeline run

Section 10: Vertex AI

Lecture 56 Introduction to Vertex AI

Lecture 57 Vertex AI automl model training

Lecture 58 Vertex AI automl model deployment

Lecture 59 Vertex AI custom model training

Lecture 60 Vertex AI custom model deployment

Lecture 61 Vertex AI custom models hyperparameters

Lecture 62 Vertex AI custom models hyperparameters SDK

Lecture 63 Vertex AI pipeline automl 1

Lecture 64 Vertex AI pipeline automl 2

Lecture 65 Vertex AI pipeline custom model

Lecture 66 Vertex AI feature store

Lecture 67 Summary of vertex AI

Data Enthusiast who wants to know what is cloud?,Beginner Data Scientists who are passionate in understanding cloud platforms.,Advanced Data Scientists who are keen to understand how to leverage GCP for ML activities,Data Scientists who already have expertise in any other cloud platforms.,Machine learning engineers who wants know the deployment and life cycle of ML models of GCP

Course Information:

Udemy | English | 6h 28m | 2.96 GB
Created by: Hemanth Kumar K

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