From the course: Data Science on Google Cloud Platform: Predictive Analytics
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Models - Google Cloud Tutorial
From the course: Data Science on Google Cloud Platform: Predictive Analytics
Models
- [Instructor] Machine learning in Cloud ML is built on models. A model is a logical container of solutions for a specific problem. For example, we have a problem of predicting spam emails for this problem, we may build a predictor model. This predictor model is stored in GCP as a Cloud ML model. All models are members of a specific GCP project. The model names are unique within a GCP project. A model can contain multiple versions of the solution. As you experiment with machine learning, you can build several versions of the same model with varying algorithms or settings. All of them can be deployed and used within a single Cloud ML model as versions. This allows for comparing model performance and doing A/B testing.
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