We'll review Cloud ML Engine's capabilities to support model training and predictions in the cloud in this video.
- [Narrator] Cloud Machine Learning Engine…is the code machine learning product on the GCP platform.…It is absolutely the focus for the rest of this course.…Cloud ML Engine provides an infrastructure…for building your own models,…deploying them on GCP,…and using them for predictive analytics at GCP scale.…You can build models exactly how you would do…in an enterprise environment,…but deploy them and use them at cloud scale…on the ML Engine.…
Cloud ML Engine supports multiple…machine learning environments including…scikit-learn, XGBoost,…scikit-learn, XGBoost,…Keras, and TensorFlow.…You can use any of these environments…and their supported programming languages…for building your models.…As with other GCP products,…ML Engine's resources are fully managed.…There is no virtual machine setup…or management work required.…ML Engine supports versions.…
It is possible to have multiple versions…of the same model at any time,…and use them simultaneously.…For clients, ML Engine supports multiple APIs.…This includes REST APIs that client programs can invoke,…
- 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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