Learn tips to reduce unexpected costs when learning about cloud services for machine learning.
- [Instructor] Whenever you're working with Cloud Services that are new to you there are a couple of tips that I always like to share. First, if possible, if you can make an entirely new GCP account just for testing rather than using a section of any production account that is always preferable. And then when you're done studying you can simply delete the account. If you can't do that you want to at least create a new GCP project. And you want to assign a user with appropriate permissions. You don't wanna have the owner user with the study account because that has the potential for abuse.
Also you wanna take advantage of the services that are covered by the free tier. When you sign up for a new GCP account you often can get $300 in GCP service credit. Now it is important to understand that not all the services that we'll be studying in this course are covered by that, so you want to look at the cost in advance, particularly around Google Compute Engine instances that are running computationally intensive machine learning algorithms such as tensor flow. And those will often be reflected in the console when you go for the set up, and we'll see that throughout the course.
You wanna be aware of potential costs. And thirdly you want to turn off all the services, in particular any GCE virtual machine instances that you might use, because those can accrue service costs pretty quickly if you forget to turn them off. And the last tip that I'll give you is to set up a billing alert in the GCP console. I usually set it for 50 to $100 and that way I get an alert pretty quickly if I forget to turn off a service that I'm no longer using.
- Hosting options: Serverless, containers, and virtual machines
- Enabling the GCP ML AIs
- Preparing data with Cloud Dataflow and Dataprep
- Modeling predictions for images, video, text to speech, and cloud translation
- Machine learning with AutoML
- Advanced machine learning and deep learning
- Machine learning architectures
Skill Level Intermediate
1. Machine Learning on Google Cloud Platform
2. Machine Learning API Services
3. Machine Learning with AutoML
Understand AutoML Vision4m 57s
4. Advanced Machine Learning
5. Machine Learning Architectures
Next steps1m 30s
- 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.