Dynatrace Intelligence service
- Concept
- 4 minutes
"Dynatrace Intelligence" is the umbrella term for all AI in Dynatrace, including generative, agentic, causal, and predictive AI. It replaces "Davis AI" and "Davis CoPilot" and expands upon their capabilities. The terms "Davis" and "CoPilot" persist in certain locations, including APIs, Swagger, scopes, and Grail. These references are still relevant for Dynatrace Intelligence.
The Dynatrace Intelligence predictive and causal AI platform service delivers a comprehensive suite of AI/ML and core statistical capabilities, seamlessly integrated into the Dynatrace platform. These essential predictive and causal analysis functions help app developers effectively address various use cases, including time series forecasting, learning anomaly detection models for time series data, and automated monitoring of metric behavior changes and anomalies.
App developers gain direct and streamlined access to all AI/ML functionalities when using Dynatrace Intelligence capabilities. This direct integration allows developers to apply these capabilities within apps, workflows, or Dynatrace functions, optimizing the efficiency and effectiveness of development processes.
Concepts
You need to know the following concepts to work with the Dynatrace Intelligence service.
Analyzer definitions
Every analyzer has a static definition, which describes its capabilities. The definition consists of the following parts: general metadata, input, and output definition.
The general metadata section contains essential information about the analyzer, such as its name, displayName, and description. An analyzer's name is fully qualified and guaranteed not to change in the future.
Input and output definitions share a standard schema describing how the input and output of a specific analyzer are structured. You can retrieve the exact structure of the input and output objects for a particular analyzer from the analyzers/<Analyzer-Name>/json-schema/input or the analyzers/<Analyzer-Name>/json-schema/output endpoints.
Analyzer execution
Flow
To execute an analyzer for the first time, we recommend the following request flow:
-
Query all analyzers: You should query all the available analyzers and choose among them using the endpoint.
-
Query a specific analyzer: Query a particular definition of an analyzer to see which functional scope and parameters the selected analyzer supports using the endpoint.
-
Query schema: Query the input and result schema of the analyzer using the and endpoint. The response is a JSON schema for the analyzer you're interested in.
-
Execute the data analyzer: Use the endpoint to execute a data analyzer. The Dynatrace Intelligence service executes analyzers asynchronously and returns a synchronous result within the defined wait period.
Execution logs
An analyzer provides execution logs if the execution fails or if additional information about the result is available. Before the Dynatrace Intelligence service executes an analyzer, it performs preliminary checks, such as input validations. If these checks fail, the analyzer is not executed and the service returns an error.
If the input matches the input definition, the request will always produce a consumable, successful result.
However, it can still happen that some analysis inside the analyzer fails for other reasons. In such cases, the analyzer provides execution logs pointing toward the problem.
Execution logs have a specific level, indicating the severity of the issue for the given analyzer. These levels can be filtered when executing an analyzer.
Example
The dt.statistics.GenericForecastAnalyzer produces a forecast for a given time series and is an excellent tool for predicting business metrics.
Given the following input:
{
"timeSeriesData": "timeseries avg(dt.host.cpu.usage)"
}
This analyzer returns the forecast predictions for the given time series:
{
"result": {
"resultId": "b433d5b1e3125ff3",
"resultStatus": "SUCCESSFUL",
"executionStatus": "COMPLETED",
"input": {
"generalParameters": {
"timeframe": "unknown-unknown",
"logVerbosity": "WARNING",
"resolveDimensionalQueryData": false
},
"forecastHorizon": 10,
"timeSeriesData": {
"expression": "timeseries avg(dt.host.cpu.usage)"
},
"coverageProbability": 0.9,
"nPaths": 200
},
"output": [
{
"timeSeriesDataWithPredictions": {
"records": [
{
"dt.davis.forecast:lower": [
20.302533297777046, 20.30119956974749, 20.299826093391346, 20.298413637147934, 20.296962988044072,
20.295474948510375, 20.293950333306114, 20.29238996657088, 20.290794679018166, 20.289165305282822
],
"dt.davis.forecast:upper": [
20.531565739653274, 20.5316575385106, 20.53178908569177, 20.531959612757507, 20.532168332681056,
20.53241444303185, 20.532697129050682, 20.53301556659801, 20.533368924960413, 20.53375636950308
],
"dt.davis.forecast:point": [
20.4170495226752, 20.416428558113733, 20.41580759355227, 20.415186628990803, 20.414565664429336,
20.41394469986787, 20.413323735306403, 20.412702770744936, 20.412081806183473, 20.411460841622006
],
"interval": "60000000000",
"timeframe": {
"end": "2023-04-03T05:42Z",
"start": "2023-04-03T05:32Z"
},
"dt.davis.internal.dataName": "avg(dt.host.cpu.usage)"
}
],
"types": [
{
"indexRange": [0, 0],
"mappings": {
"dt.davis.forecast:lower": {
"type": "array",
"types": [
{
"indexRange": [0, 9],
"mappings": {
"element": {
"type": "double"
}
}
}
]
},
"timeframe": {
"type": "timeframe"
},
"dt.davis.forecast:upper": {
"type": "array",
"types": [
{
"indexRange": [0, 9],
"mappings": {
"element": {
"type": "double"
}
}
}
]
},
"dt.davis.forecast:point": {
"type": "array",
"types": [
{
"indexRange": [0, 9],
"mappings": {
"element": {
"type": "double"
}
}
}
]
},
"interval": {
"type": "duration"
},
"dt.davis.internal.dataName": {
"type": "string"
}
}
}
]
},
"forecastQualityAssessment": "valid",
"analysisStatus": "OK",
"analyzedTimeSeriesQuery": {
"expression": "timeseries avg(dt.host.cpu.usage)",
"timeframe": {
"startTime": "2023-04-03T03:31:32.718Z",
"endTime": "2023-04-03T05:31:32.718Z"
}
}
}
]
}
}