This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. 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.
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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.
This course is part of the Google Cloud Data Engineer Learning Path Professional Certificate.
What you'll learn
- 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.
- Create ML models without coding using AutoML.
Syllabus
- Introduction
In this module, we introduce the course and agenda.
- Introduction to Analytics and AI
This module talks about ML options on Google Cloud.
- Prebuilt ML Model APIs for Unstructured Data
This module focuses on using pre-built ML APIs on your unstructured data.
- Big Data Analytics with Notebooks
This module covers how to use Notebooks.
- Production ML Pipelines
This module covers building custom ML models and introduces Vertex AI and AI Hub.
- Custom Model Building with SQL in BigQuery ML
This module covers BigQuery ML.
- Custom Model Building with AutoML
Custom model building with AutoML.
- Summary
This module recaps the topics covered in the course.
- Course Resources
PDF links to all modules.
