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Genre: eLearning
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
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
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