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 Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs. This is the fourth course of the Data Engineering on Google Cloud series.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.

Table of Contents

• Introduction
  1min
• Introduction to Analytics and AI
  8mins
• Prebuilt ML Model APIs for Unstructured Data
  12mins
• Big Data Analytics with Notebooks
  7mins
• Production ML Pipelines with Kubeflow
  10mins
• Custom Model building with SQL in BigQuery ML
  12mins
• Custom Model Building with AutoML
  24mins
• Summary
  0mins
• Course Resources
  0mins