Machine Learning on Google Cloud (Vertex AI) - Hands on!
- Descripción
- Currículum
- Reseñas
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.
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13Introduction to storage & gcsVideo lesson
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14Persistent disk & FilestoreVideo lesson
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15Demo on google cloud storageVideo lesson
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16Introduction to database, cloud sql & bigtableVideo lesson
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17Spanner, Memory store & FirebaseVideo lesson
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18Demo: Cloud SQL creationVideo lesson
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19Demo: Cloud SQL accessVideo lesson
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20Summary of storage & databaseVideo lesson
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24Introduction to analyticsVideo lesson
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25Pubsub & DataprocVideo lesson
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26Dataflow, Bigquery & DataprepVideo lesson
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27Demo: Bigquery 1Video lesson
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28Demo: Bigquery 2Video lesson
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29Demo: Bigquery 3Video lesson
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30Demo: Dataprep 1Video lesson
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31Demo: Dataprep 2Video lesson
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32Summary of analyticsVideo lesson
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33Introduction to AI servicesVideo lesson
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34Introduction to AutomlVideo lesson
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35Automl Tables model trainingVideo lesson
https://www.kaggle.com/saisaathvik/used-cars-dataset-from-cardekhocom?select=cardekho_updated.csv
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36Automl Tables deployment & batch predictionsVideo lesson
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37Automl Tables online predictionsVideo lesson
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38Automl Vision model trainingVideo lesson
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39Automl Vision model deploymentVideo lesson
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40Automl language model trainingVideo lesson
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41Automl language model deploymentVideo lesson
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42Automl pre built modelsVideo lesson
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43Summary of AutomlVideo lesson
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44Introduction to AI PlatformVideo lesson
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45AI Platform NotebooksVideo lesson
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46AI Platform model deployment (console)Video lesson
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47AI Platform custom predictors 1Video lesson
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48AI Platform custom predictors 2Video lesson
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49Introduction to jobs on AI Platform JobsVideo lesson
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50AI Platform Jobs creation & submissionsVideo lesson
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51AI Platform Jobs evaluation & deploymentVideo lesson
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52Introduction to pipelines on AI PlatformVideo lesson
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53AI Platform pipeline docker image creationVideo lesson
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54AI Platform pipeline configure code walkVideo lesson
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55AI Platform pipeline runVideo lesson
