Share Description
In this course, you will :
- demonstrates how to use GCP's power to generate predictions for your business.
- Begin by investigating the various predictive analytics tools and features available in GCP, such as Cloud Dataproc, Cloud ML Engine, and machine learning APIs such as Cloud Translation, Cloud Vision, and Cloud Video Intelligence.
- Investigate how to build, train, and deploy models to make predictions.
- Learn best practises for predictive model cost control, testing, and performance monitoring.
Syllabus :
1. ML Options in GCP
- Cloud Dataproc
- Cloud ML Engine
- Cloud Natural Language
- Cloud Translation
- Cloud Vision
- Cloud Video Intelligence
- Cloud Dialogflow
2. Cloud ML Basics
- Models
- Model versions
- Jobs
- Predictive analytics process
3. Model Building with Cloud ML
- Understanding input data
- Build and test model locally
- Upload files to Cloud Storage
- Modify code to work with GCP
- Creating a training package
- Running training synchronously
- Training using jobs
4. Predictions in Cloud ML
- Creating a deployment model
- Creating a model version
- Creating a prediction dataset
- Running a prediction
5. Cloud ML Best Practices
- Cost control
- Local testing
- Performance monitoring
