In this video, learn about the Cloud ML models and how they are used.
- [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.…
- Evaluating the machine learning tools in GCP
- Understanding the predictive analytics process
- Building models
- Training models with jobs
- Building and running predictions
- Best practices for cost control, testing, and performance monitoring
Skill Level Intermediate
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. ML Options in GCP
2. Cloud ML Basics
3. Model Building with Cloud ML
4. Predictions in Cloud ML
5. Cloud ML Best Practices
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