Machine Learning on Google Cloud (Vertex AI & AI Platform) - Learn how to build & deploy ML/DL models using GCP components AutoML, AI Platform and Vertex AI
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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
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 of
hyperparameter optimization step in the custom model. Kubeflow pipelines creation using AutoML & custom models. You will also work on the Feature store.
Who this course is for:
- 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