This pull request introduces several improvements and new features across the backend and catalog packages, with a focus on enhancing machine learning capabilities and optimizing file handling. The most significant changes include the addition of Teachable Machine integration, a new image embedding node, and substantial optimizations to the bit file download and upload logic.

Machine Learning & Catalog Enhancements:

• Added support for Google Teachable Machine models in the catalog, including registration of the new node for workflow integration (teachable_machine), and updated documentation with a full tutorial blog post for users. [1] [2]
• Introduced a new EmbedImageNode for image embedding, allowing images to be processed and embedded using loaded models within workflows. This includes the node implementation and registration in the catalog. [1] [2]
• Added dependencies for TensorFlow Lite support in the catalog, enabling local inference for Teachable Machine and other TFLite models.

Backend File Handling Optimizations:

• Refactored the bit file download and upload logic to support efficient single PUT uploads for small files and optimized multipart uploads for larger files, including proper chunking and buffer management for streaming downloads. This reduces overhead and improves reliability for both ranged and non-ranged downloads. [1] [2] [3]
• Improved progress reporting and error handling in the download/upload process, ensuring accurate status updates and robust handling of edge cases.

Dependency Updates:

• Added the tracing crate to the Lambda runtime for enhanced observability and debugging in AWS Lambda functions.
• Updated imports in the bit admin route to include Bytes for efficient buffer management during multipart uploads.

These changes collectively enhance the platform’s AI workflow capabilities, improve file handling efficiency, and strengthen integration with modern machine learning tools.