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Product Ideas Pipeline

1170 Ideas

Feature Request: Allow Metadata Addition/Updates for Existing Cognite FunctionsPlanned for development

I have a use case where I need to retrieve all Cognite Functions and perform operations on a subset of them based on specific criteria. To achieve this, I want to use the metadata field in Cognite Functions as a filtering mechanism. However, the current limitation in the Cognite Functions API does not allow updating metadata for existing functions or adding metadata to functions that were created without it. Current LimitationCurrently, the only way to modify metadata for a function is to delete and re-upload the function. This approach has significant drawbacks:Loss of Schedules: Deleting a function also deletes any associated schedules, which need to be recreated manually. Loss of History: Run history is also lost upon re-uploading, impacting the ability to analyze past runs.Proposed FeatureIntroduce functionality in the Cognite Functions API that allows:Updating the metadata field for existing functions. Adding metadata to existing functions that do not have metadata.BenefitsImproved Filtering: Users can dynamically tag or categorize functions with metadata and retrieve only relevant ones for specific operations. Preservation of Schedules and History: Avoids the need to delete and recreate functions, preserving associated schedules and run history. Enhanced Flexibility: Makes Cognite Functions more adaptable to evolving use cases without requiring disruptive workflows.Use Case ExampleIn my use case, I would use metadata to tag functions based on their purpose (e.g., [CALC], [ANALYTICS]) and filter them efficiently. The inability to update/add metadata on existing Cognite function prevents me from easily maintaining and managing my function. 

Tranning: Cognite Advanced 3D Model ContextualizationGathering Interest

Develop an advanced training program to equip users with skills for contextualizing point cloud data, focusing on both detected and undetected objects. The training should address gaps in traditional modeling approaches by providing practical, hands-on experience with diverse scenarios. Challenges Addressed:Limited automation in object detection, requiring significant manual effort. Difficulty in contextualizing objects that remain undetected in raw point cloud data. Inability to handle diverse and complex industrial scenarios effectively.Hands-On Examples and Exercises:  Detected Objects: Import and preprocess a point cloud dataset. Use AI-driven tools to identify and classify detected objects. Automatically link detected objects to an asset hierarchy, metadata, or P&ID diagrams.   Undetected Objects: Demonstrate manual workflows for identifying undetected objects within the point cloud. Tag, classify, and link undetected objects using the training interface. Show how to create relationships between manually contextualized objects and other datasets Use examples from different industries (oil & gas, manufacturing, energy, etc.) to cover various asset types.Include examples of objects partially occluded, poorly defined, or from atypical asset classes. Resource Materials:Develop a library of sample datasets, best practices, and case studies for ongoing reference.