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.
Overview
Syllabus
1. Introduction
- In this module, we introduce the course and agenda.
2. Introduction to Analytics and AI
- This module talks about ML options on Google Cloud.
3. Prebuilt ML Model APIs for Unstructured Data
- This module focuses on using pre-built ML APIs on your unstructured data.
4. Big Data Analytics with Notebooks
- This module covers how to use Notebooks.
5. Production ML Pipelines
- This module covers building custom ML models and introduces Vertex AI and AI Hub.
6. Custom Model Building with SQL in BigQuery ML
- This module covers BigQuery ML.
7. Custom Model Building with AutoML
- Custom model building with AutoML.
8. Summary
- This module recaps the topics covered in the course.
9. Course Resources
- PDF links to all modules.
Taught by
Google Cloud Training
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