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docs/data-ai/_index.md

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{{< cards >}}
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{{% card link="/data-ai/data/query/" noimage="true" %}}
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{{% card link="/data-ai/data/visualize/" noimage="true" %}}
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{{% card link="/data-ai/data/advanced/alert-data/" noimage="true" %}}
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{{% card link="/data-ai/data/alert-data/" noimage="true" %}}
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{{% card link="/data-ai/data/export/" noimage="true" %}}
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{{< /cards >}}
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{{% card link="/data-ai/ai/train-tflite/" noimage="true" %}}
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{{% card link="/data-ai/ai/run-inference/" noimage="true" %}}
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{{% card link="/data-ai/ai/alert/" noimage="true" %}}
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{{% card link="/data-ai/ai/act/" noimage="true" %}}
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{{% card link="/data-ai/inference/run-inference/" noimage="true" %}}
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{{% card link="/data-ai/inference/alert/" noimage="true" %}}
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{{% card link="/data-ai/inference/act/" noimage="true" %}}
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docs/data-ai/ai/_index.md

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---
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linkTitle: "Leverage AI"
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title: "Leverage AI"
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linkTitle: "Train an ML model"
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title: "Train an ML model"
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weight: 300
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layout: "empty"
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type: "docs"

docs/data-ai/ai/deploy.md

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On its own the ML model service only runs the model.
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After deploying your model, you need to configure an additional service to use the deployed model.
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For example, you can configure an [`mlmodel` vision service](/operate/reference/services/vision/) to visualize the inferences your model makes.
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Follow our docs to [run inference](/data-ai/ai/run-inference/) to add an `mlmodel` vision service and see inferences.
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Follow our docs to [run inference](/data-ai/inference/run-inference/) to add an `mlmodel` vision service and see inferences.
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For other use cases, consider [creating custom functionality with a module](/operate/get-started/other-hardware/).
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docs/data-ai/ai/train-tflite.md

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## Next steps
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Now your machine can make inferences about its environment.
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The next step is to [deploy](/data-ai/ai/deploy/) the ML model and then [act](/data-ai/ai/act/) or [alert](/data-ai/ai/alert/) based on these inferences.
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The next step is to [deploy](/data-ai/ai/deploy/) the ML model and then [act](/data-ai/inference/act/) or [alert](/data-ai/inference/alert/) based on these inferences.
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See the following tutorials for examples of using machine learning models to make your machine do things based on its inferences about its environment:
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docs/data-ai/ai/train.md

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To use your new model with machines, you must [deploy it](/data-ai/ai/deploy/) with the appropriate ML model service.
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Then you can use another service, such as the vision service, to [run inference](/data-ai/ai/run-inference/).
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Then you can use another service, such as the vision service, to [run inference](/data-ai/inference/run-inference/).

docs/data-ai/ai/advanced/upload-external-data.md renamed to docs/data-ai/ai/upload-external-data.md

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- /how-tos/upload-data/
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date: "2024-12-04"
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description: "Upload data to the Viam app from your local computer or mobile device using the data client API, Viam CLI, or Viam mobile app."
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prev: "/data-ai/ai/act/"
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prev: "/data-ai/inference/act/"
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When you configure the data management service, Viam automatically uploads data from the default directory `~/.viam/capture` and any directory you configured.

docs/data-ai/capture-data/advanced/_index.md

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docs/data-ai/capture-data/capture-other-sources.md

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docs/data-ai/capture-data/capture-sync.md

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For instructions on configuring data capture and sync with JSON, go to [Advanced data capture and sync configurations](/data-ai/capture-data/advanced/advanced-data-capture-sync/) and follow the instructions for JSON examples.
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For instructions on configuring data capture and sync with JSON, go to [Advanced data capture and sync configurations](/data-ai/reference/advanced-data-capture-sync/) and follow the instructions for JSON examples.
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## View captured data
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- [Conditional sync](/data-ai/capture-data/conditional-sync/)
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- [Retention policies](/data-ai/capture-data/advanced/advanced-data-capture-sync/#cloud-data-retention)
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- [Sync optimization](/data-ai/capture-data/advanced/advanced-data-capture-sync/#sync-optimization)
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- [Retention policies](/data-ai/reference/advanced-data-capture-sync/#cloud-data-retention)
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- [Sync optimization](/data-ai/reference/advanced-data-capture-sync/#sync-optimization)
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## Next steps
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For more information on available configuration attributes and options like capturing directly to MongoDB or conditional sync, see [Advanced data capture and sync configurations](/data-ai/capture-data/advanced/advanced-data-capture-sync/).
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For more information on available configuration attributes and options like capturing directly to MongoDB or conditional sync, see [Advanced data capture and sync configurations](/data-ai/reference/advanced-data-capture-sync/).
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To leverage AI, you can now [create a dataset](/data-ai/ai/create-dataset/) with the data you've captured.

docs/data-ai/capture-data/conditional-sync.md

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title: "Conditional cloud sync"
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title: "Conditional sync"
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linkTitle: "Conditional sync"
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description: "Trigger cloud sync to sync captured data when custom conditions are met."
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languages: []
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viamresources: ["sensor", "data_manager"]
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platformarea: ["data", "registry"]
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next: /data-ai/reference/advanced-data-capture-sync/
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date: "2024-12-04"
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