Review best practices for building and testing models locally before deploying on Cloud ML.
- [Instructor] It's better to use local,…fixed-cost, or low cost resources…as much as possible when using GCP.…What are some of the tips to do it?…Well, try to use as small a dataset as possible…for model building.…The smaller the dataset, the lesser…the resource requirements.…You need to have a large enough dataset…to build or create models,…but you can grow the dataset after the initial trials.…Use non-GCP resources like laptops.…Today's laptops can handle significant amounts of data…locally, so use them first.…
Firm up the model before moving to GCP.…Use compute engine resources as just VMs…for model building and testing.…This is outside of Cloud ML,…and tends to be cheaper than using Cloud ML directly.…Contact us to ensure model consistency…before deploying them on Cloud ML.…Cloud ML should be used as the main platform…for predictions, not trials and trainings.…If you focus on working out the small kinks locally first,…you can scale to the cloud…without a huge hit to your checkbook.…
- 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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