Amazon Lookout for Metrics uses machine learning (ML) to automatically detect and diagnose anomalies (i.e. outliers from the norm) in business and operational data, such as a sudden dip in sales revenue or customer acquisition rates. In a couple of clicks, you can connect Amazon Lookout for Metrics to popular data stores like Amazon S3, Amazon Redshift, and Amazon Relational Database Service (RDS), as well as third-party SaaS applications, such as Salesforce, Servicenow, Zendesk, and Marketo, and start monitoring metrics that are important to your business.
That said, it is still a computer system and at the end of the day it will respond accordingly to the specific inputs it is given. This is designed to be a short guide to help you think through the data you provide Lookout for Metrics and to better understand what the results from Lookout for Metrics mean. This will be done using a few real world-esque scenarios.
If you are going to be looking out for them, it would help to know what they are at first. Within Lookout for Metrics there are 3 components to your datasets, together they help shape your Metrics.
They are:
Timestamp- This is required, all entries in this service are required to start with a timestamp of when the remaining columns were relevant or occurred.Dimensions- These are categorical columns, you can have up to 5 of them, keep in mind they are combined together to refer to a specific entity. For example, if your domains arelocationandemergency typeyour data could look like this:
| timestamp | location | emergency type |
|---|---|---|
| 01/10/2022 10:00:00 | 123 Interesting Ave | smoke_alarm |
| 01/10/2022 10:00:00 | 123 Interesting Ave | water_sensor |
| 01/10/2022 10:00:00 | 745 Interesting Ave | smoke_alarm |
| 01/10/2022 10:00:00 | 745 Interesting Ave | water_sensor |
From this dataset we have 2 dimensions(location and emergency type) and when we start to think about the total number of possible metrics(full calculation to come) we can see there are 2 distinct locations and 2 distinct emergency types.
Measures- These are the numerical columns where real observable numbers are placed. These numbers are bound to a specific unique set of domains. You can also have up to 5 of these columns. Now expanding our earlier dataset with 2 additional numerical columns oftotalandfixed.
| timestamp | location | emergency type | total | fixed |
|---|---|---|---|---|
| 01/10/2022 10:00:00 | 123 Interesting Ave | smoke_alarm | 10 | 8 |
| 01/10/2022 10:00:00 | 123 Interesting Ave | water_sensor | 10 | 10 |
| 01/10/2022 10:00:00 | 745 Interesting Ave | smoke_alarm | 10 | 10 |
| 01/10/2022 10:00:00 | 745 Interesting Ave | water_sensor | 10 | 7 |
Lookout for Metrics with this dataset has 8 metrics. How did we get this number?
A Metric is a unique combination of categorial entries and 1 numerical value.
The formula to calculate the total number of metris is: Unique(domain1) * Unique(domain2) * Number of measures. So in this case that would be:
2 * 2 * 2 or 8.
At present Lookout for Metrics can support a maximum of 50,000 metrics per Detector, which is the trained model assigned to a particular set of data. So if you wanted to track more of them than 50,000, you would simply segment your data into multiple Detectors.
Like all great things powered by Machine Learning: It depends!