Learn best practices and tips to avoid unwanted AWS service billing charges.
- [Instructor] In this course we're going to be working with AWS cloud based services. Now there are some best practices that I like to share. When you're learning it's always best to use a dedicated user account. In fact, it's best to use a dedicated Amazon account if you can setup a separate account. It's not always he case, but that really allows for a clean separation between any test and production environments. At minimum you want to use a unique user login. Although many of these services will be included partially in the free tier so that you can try them out, not all services are included so you really want to understand what kind of charges you could be racking up if you turn the services on, and the biggest tip I'll give you is it's usually not expensive if you just try something out quickly.
Where it can become expensive is if and when you forget to turn the service off after you're done learning about it. To round out this discussion of tips, I want to take you out to the Amazon console and show you where the billing dashboard is. So here is the Amazon console, and if you click on the dropdown next to where your login is shown, you'll see my billing dashboard. Now there's many different ways that you can get service cost information from Amazon, but this is probably the simplest.
Now you do need to have a high level of permission, so if you click on this link and you don't have permission to view it, then you want to talk to your organizational administrator, but this will help you, and you can see I've done some other testing on this account, and I've had some charges occurring over the past period. So you can scroll down, and you can see where the charges are coming from, which of the services. In addition to viewing the dashboard, a best practice is to set a budget. So you just click on budgets and fill in the information there to get notified if your service charges go over a certain amount.
You really want to follow these best practices particularly when you're working with computationally intensive services like those we're going to work with in machine learning 'cause you can run up charges pretty quickly if you forget to turn high powered servers off for example.
- How machine learning is used in analytics
- AWS AI servers vs. platforms
- Predicting using Polly text-to-speech
- Predicting using Rekognition for video
- Using Lex to build a conversational application
- Using the AWS Machine Learning service to train, host, and predict
- Working with MXNet in Databricks
- Working with EMR for machine learning