khalidw

App-Owns-Data-Starter-Kit

App-Owns-Data Starter Kit is a developer sample demonstrating common design techniques used in App-Owns-Data embedding.

AZ-104-MicrosoftAzureAdministrator

AZ-104 Microsoft Azure Administrator

Capstone-Project-Azure-Machine-Learning-Engineer

This is the Capstone project (last of the three projects) required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we use a dataset external to Azure ML ecosystem. Azure Machine Learning Service and Jupyter Notebook is used to train models using both Hyperdrive and Auto ML and then the best of these models is deployed as an HTTP REST endpoint. The model endpoint is also tested to verify if it is working as intented by sending an HTTP POST request. Azure ML Studio graphical interface is not used in the entire exercise to encourage use of code which is better suited for automation and gives a data scientist more control over their experiment.

media-services-dotnet-functions-integration

Sample Azure Functions for use with Azure Media Services. Ingest from Azure Blobs, encode and output to Azure Blobs, monitor encoding progress, and use WebHooks or Queues to hook into the workflow.

MicrosoftML-ProjectShowcasing

A repository to keep all open sources projects that created by individuals or study groups of Microsoft ML Scholarship

MOT16_Annotator

This tool takes video as an input and allows the user to create bounding boxes on each frame, these bounding boxes are saved to a gt.txt file in MOT16 format. The purpose of this file is to allow the user to manually create ground truth for their custom dataset. This ground truth file can then be used in conjunction with tracker output file to generate MOT metrics to gauge the performance of tracker on custom data.

nd064_course_1

Operationalizing-Machine-Learning

This is second of the three projects required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we create, publish and consume a Pipeline. We also explore ML model deployment as an HTTP REST API endpoint, swagger API documentation, apache benchmarking of the deployed endpoint and consumption of the endpoint using JSON documents as HTTP POST request.

py-motmetrics

:bar_chart: Benchmark multiple object trackers (MOT) in Python

social_distancing

To help ensure social distancing, we have developed an AI-enabled social distancing detection tool that can detect if people are keeping a safe distance from each other by analyzing real time video streams from the camera.

khalidw/MOT16_Annotator

This tool takes video as an input and allows the user to create bounding boxes on each frame, these bounding boxes are saved to a gt.txt file in MOT16 format. The purpose of this file is to allow the user to manually create ground truth for their custom dataset. This ground truth file can then be used in conjunction with tracker output file to generate MOT metrics to gauge the performance of tracker on custom data.

khalidw/Capstone-Project-Azure-Machine-Learning-Engineer

This is the Capstone project (last of the three projects) required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we use a dataset external to Azure ML ecosystem. Azure Machine Learning Service and Jupyter Notebook is used to train models using both Hyperdrive and Auto ML and then the best of these models is deployed as an HTTP REST endpoint. The model endpoint is also tested to verify if it is working as intented by sending an HTTP POST request. Azure ML Studio graphical interface is not used in the entire exercise to encourage use of code which is better suited for automation and gives a data scientist more control over their experiment.

khalidw/social_distancing

To help ensure social distancing, we have developed an AI-enabled social distancing detection tool that can detect if people are keeping a safe distance from each other by analyzing real time video streams from the camera.

khalidw/py-motmetrics

:bar_chart: Benchmark multiple object trackers (MOT) in Python

khalidw/Operationalizing-Machine-Learning

This is second of the three projects required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we create, publish and consume a Pipeline. We also explore ML model deployment as an HTTP REST API endpoint, swagger API documentation, apache benchmarking of the deployed endpoint and consumption of the endpoint using JSON documents as HTTP POST request.

khalidw/App-Owns-Data-Starter-Kit

App-Owns-Data Starter Kit is a developer sample demonstrating common design techniques used in App-Owns-Data embedding.

khalidw/AZ-104-MicrosoftAzureAdministrator

AZ-104 Microsoft Azure Administrator

khalidw/media-services-dotnet-functions-integration

Sample Azure Functions for use with Azure Media Services. Ingest from Azure Blobs, encode and output to Azure Blobs, monitor encoding progress, and use WebHooks or Queues to hook into the workflow.

khalidw/MicrosoftML-ProjectShowcasing

A repository to keep all open sources projects that created by individuals or study groups of Microsoft ML Scholarship

khalidw/nd064_course_1

khalidw/Optimizing_a_Pipeline_in_Azure-ML

This is first of the three projects required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we build and optimize an Azure ML pipeline using the Python SDK and a provided Scikit-learn model. This model is then compared to an Azure AutoML run.

khalidw/PBI-Embedded-App-Owns-Data-with-RLS

Power BI Embedded, Embed Token REST API Documentation as well as developer samples for RLS implementation in embedded solution for App Owns data (customers)

khalidw/qiskit-translations

Home of Qiskit documentation translations

khalidw/snowflake_assistant

this repo contain code for snowflake hackathon: future of AI is OPEN

khalidw/sort

Simple, online, and realtime tracking of multiple objects in a video sequence.