In this video, see how to create a model version and set up inputs, parameters, and scaling.
- [Narrator] Now that we have created a model,…we need to create a specific version of the model…in order to deploy the Python model we built earlier.…On the model record in the browser,…click on the three dots and select "Create Version."…A Version creation page shows up.…Let us enter the following information.…For the name I'm going to enter, "Website Propensity_V1."…Description would be,…"The first model built using Naive Bayes in Python."…We then choose the specific Python version to run the model.…
We will choose 2.7, this is the version that is supported…at the time of this recording.…Next comes the framework for building the model.…We will choose, "scikit learn," and then we choose the…framework version to be 0.19.1.…Next comes the version of cloud ML to use.…We will choose the latest, 1.9.…Then we choose the machine type that indicates a type of…machine we need to run the model.…
Machines of higher capacity usually cost more to run.…We are going to choose a single CPU machine.…Finally, we need to import the URL of the location…
- 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
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.