Train ML with the Right Solution
Learning Objectives
After completing this unit, you’ll be able to:
- Explain the use case for BigQuery ML.
- Explain the use case for Pre-trained API.
- Explain the use case for AutoML.
- Explain the use case for custom ML training.
In the past, AI and ML were not accessible to the business at large—the technology was too complex and required extreme specialization. This is changing. In this unit, you explore four options to build ML models with Google Cloud: BigQuery ML, pretrained APIs, AutoML, and custom training.
Watch the following videos from Google Cloud. The quizzes at the end of each unit ask questions about the content of these videos. Be sure to watch so you get the information you need to answer the questions at the end of each unit.
Train with BigQuery ML
Train with Pre-trained APIs
Train with AutoML
Train with Custom Models
Learn About TensorFlow
Explore End-to-End AI Solutions
Find the Right Solution
Did You Watch the Videos?
Remember, the quiz asks about the videos in this unit. If you haven’t watched the videos yet, go back and do that now to ensure you’re ready to take the quiz.
Sum It Up
As AI technology matures, solutions and tools become more and more accessible. This enables more development to be done without requiring highly specialized teams. But that means there are more options introduced into the market day by day—businesses need to clearly define their needs and use cases, and evaluate potential solutions carefully.
In this module, Google Cloud walked you through fundamentals AI and ML concepts, basic use cases, and even reviewed the solutions available on the Google Cloud platform.
Get Certified
This module is part of Google Cloud's Cloud Digital Leader learning and certification program. Complete the trail to start your preparation for taking the exam.