Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
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Differentiate between ML, AI and Deep Learning.
Discuss the use of ML API’s on unstructured data.
Execute BigQuery commands from Notebooks.
Create ML models by using SQL syntax in BigQuery and without coding using AutoML.
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Программа курса: что вы изучите
Introduction
Introduction to Analytics and AI
Prebuilt ML model APIs for Unstructured Data
Big Data Analytics with Notebooks
Production ML Pipelines
Custom Model building with SQL in BigQuery ML
Custom Model Building with AutoML
Summary
Рецензии
- 5 stars69,01 %
- 4 stars23,94 %
- 3 stars4,46 %
- 2 stars1,45 %
- 1 star1,11 %
Лучшие отзывы о курсе SMART ANALYTICS, MACHINE LEARNING, AND AI ON GOOGLE CLOUD
Very good ML course to introduce students with Google Cloud machine learning capabilities. Maybe there should be a lab for AutoML (after video lessons), as it exists on Qwiklab platform.
Great course to have a complete overview of the GCP platform and components.
Course gives basic overview on ML concepts and how we can do using GCP. Good enough for Data Engineer to understand
Good structure and overview of things which can be accomplished in GCP Analytics
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