In this video, see how to run training in Cloud ML asynchronously using jobs.
- [Instructor] Let's see how see can set up…training jobs in Cloud ML,…that can work on large training data sets…and run the process asynchronously.…To achieve this, I will first create a Shell Script…called submit_job.sh with the following content.…In this, we first set the project ID,…under whose context this job needs to be run.…This is the GCP project ID.…We set the bucket ID to the path where…you want all the files to be created and stored.…
The job name points to a unique name…that can be used by the system to create…the data cree, and the model for it.…We are upending the date, with year, month, day,…hour, minute, second of it,…so we know when the model was created.…The job_dir is essentially a temporary data cree,…that would be used by Cloud ML for its internal purposes.…The training_package_path points to the actual…path under which we created the training package.…
The main_trainer_module points to the module…name that was created in the same package.…Region specifies the region under which…you want to set up a region for you…
- 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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