Review jobs in Cloud ML and how they help to execute training and predictions asynchronously in this video.
- [Instructor] Training models usually run…for a long amount of time.…In Cloud ML, you can manage them using Jobs.…Jobs are long-running operations that can either be used…for training models or for batch predictions.…They are asynchronous in nature.…They can either be created through the command line…or through the REST API.…You can set up training jobs…that can consume data from Cloud Storage…and then store the models built back into Cloud Storage.…
You can run prediction batch jobs…that consume models and data from Cloud Storage…and write the predictions back into Cloud Storage.…Cloud ML provides user interfaces…through which you can track the status of a given job.…It also provides access to log files generated by your code.…This helps in troubleshooting any issues that are found.…
- 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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