https://github.com/kelvininc/app-samples

https://github.com/kelvininc/app-samples

Last synced: 30 days ago
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• Host: GitHub
• URL: https://github.com/kelvininc/app-samples
• Owner: kelvininc
• Created: 2023-10-16T17:42:24.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-08-26T23:35:20.000Z (5 months ago)
• Last Synced: 2024-08-27T02:09:09.055Z (5 months ago)
• Language: Python
• Size: 49.3 MB
• Stars: 7
• Watchers: 9
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
![Kelvin Logo](logo.png)

# Welcome to Kelvin SDK - App Samples

This repository contains sample applications that demonstrate how to use the **Kelvin SDK**. 

We recommend that you start first by reading the official Kelvin Documentation on https://docs.kelvin.ai.

# Applications

| Application | Type | Level | Description |

| ----------- | ---- | ----- | ----------- |

| [Azure Data Lake Gen2 Uploader](azure-data-lake-uploader/) | Data Uploader | Intermediate | Uploads streaming data to Azure Data Lake Storage Gen2. |

| [Casting Defect Detection using Computer Vision](casting-defect-computer-vision/) | Computer Vision | Intermediate | Leverages computer vision and a Tensorflow-based model to identify and analyze manufacturing defects in casting processes. |

| [CSV Stream Publisher](csv-stream-publisher/) | CSV Stream Publisher | Beginner | Ingests Data from a CSV file and publishes it to the Kelvin platform. |

| [Databricks Delta Table Uploader](databricks-delta-table-uploader/) | Data Uploader | Intermediate |  Uploads streaming data to Databricks Delta Table. |

| [Databricks Volume Uploader](databricks-volume-uploader/) | Data Uploader | Intermediate |  Uploads streaming data to Databricks Volume. |

| [Event Detection](event-detection/) | Event Detection | Beginner | Monitors streaming data to detect and respond to events exceeding pre-set thresholds by emitting a Control Change output. |

| [Event Detection (Complex)](event-detection-complex/) | Event Detection | Intermediate | Monitors streaming data to detect and respond to events exceeding pre-set thresholds by emitting a Control Change or Recommendation output. This example also leverages Asset Parameters and App Configuration to make the application more dynamic. |

| [Multi-Objective Optimization ML](multi-objective-optimization-ml/) | Machine Learning | Intermediate | Implements a multi-objective optimization problem using machine learning techniques. |

| [Rolling Window DataFrame](rolling-window-dataframe/) | Rolling Window | Beginner | Demonstrates the creation of a rolling window using Pandas to manage and analyze time-series data effectively. |

# Contributing

1. Fork the project.

2. Create your feature branch (git checkout -b feature/YourFeature).

3. Commit your changes (git commit -m 'Add some feature').

4. Push to the branch (git push origin feature/YourFeature).

5. Open a pull request.