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.