Azure DevOps 101

Clone from Azure DevOps repository

1-1. Input Password, Confirm Password
1-2. Save Git Credentials
1-3. From your shell, git clone ****
1-4. Password is required, and then input the one you set up

Sometimes the Password above does not work, with the error message like "fatal: Authentication failed for 'https://****' ". In that case, please try creating a personal token

2-1. Input Password, Confirm Password
2-2. Click "Create a Personal access token"
2-3. Click "+New Token"
2-4. Input name and select "Full access", and click "Create"
2-5. The token is displayed on the screen. The warning message is "Make sure you copy the above token now. We don't store it and you will not be able to see it again"
2-6. From your shell, git clone ****
2-7. Input the token obtained above

Azure Machine Learning Service - deploy to webservice

Tensorflow Object Detection API - Containerize the web service

Tensorflow Object Detection API - Inference Web Service

It is great to create REST API to access from anywhere to use your machine learning model. Flask framework makes it easy to create a web service with python. All the code is include in object_detection_app_test1.py.

Tensorflow Object Detection API - Inference

After you get a machine learning model by training on Tensorflow, you can run that model and receive the detection result. The source code is here on Github. (https://github.com/koheikawata/objectdetectiontest). Below are examples of inputs and outputs of "object_detection_inference_test1.py"

Precision and Recall

Precision




Recall

Tensorflow Object Detection API - TFRecord creation

Understand how to create TFRecords is an important point when you use Tensorflow Object Detection API. In my Github repo (https://github.com/koheikawata/objectdetectiontest), you can find what directory structure looks like.

``` . └── research ├── object_detection | ├── legacy | | ├── train.py | | └── eval.py | └── test1 | ├── eval_images | | └── export-image-0-b'IMG_0000'.png | ├── checkpoints | | └── model.ckpt-4495.data-00000-of-00001 | | └── model.ckpt-4495.index | | └── model.ckpt-4495.meta | ├── output | | └── saved_model | | └── frozen_inference_graph.pb | ├── Annotations | | └── ***.xml | ├── JPEGImages | | └── ***.jpg | ├── flask_pics | ├── pascal_label_map.pbtxt | ├── train.record | └── val.record ├── faster_rcnn_resnet101_coco_2018_01_28 | └── model.ckpt ├── faster_rcnn_resnet101_coco_test1.config ├── create_tf_record_test1.py ├── object_detection_app_test1.py ├── object_detection_inference_test1.py ├── object_detection_app_test1_docker.py └── Dockerfile ```

Data Factory connection

I would like to share some pitfalls when you connect FTP server to Azure Data Factory as I had some troubles connecting Data Factory to data sources.

Microsoft Learn - Xamarin + Twilio

I ran through "Share your location as a text message using Azure Functions and Twilio" at Microsoft Learn to understand Xamarin. It looks not just about Xamarin, and you cannot learn the whole picture of Xamarin from this, but it is a good practice if you are not familiar with client apps.They system figure is below.

HttpRequest and HttpRequestMessage for Azure Functions

I was working on Microsoft Learn, "Share your location as a text message using Azure Functions and Twilio", and faced a problem on HttpRequest class for Azure Functions. Usually Azure Functions takes HttpRequest class for extracting request. However, the tutorial described HttpRequestMessage. I was really confused because HttpRequestMessage works partly, but it does not support Body property.

Functions binding Cosmos DB, C# -- 2

Sample Code
4. Extract specific json properties
5. Read multiple documents

Functions binding Cosmos DB, C# -- 1

I was trying Cosmos DB binding for Azure Functions, and want to share what I learned. This is a basic skill for Azure users because it uses Cosmos DB that can handle Json files easily, and Azure Functions that manages http requests. Below is the system configuration.

LUIS(Language Understanding Intelligent Service) -- Simple and List Entity

LUIS(Language Understanding Intelligent Service) is a Azure-based API for natural language processing. It is very useful and easy to apply conversational system. However, LUIS has so many features and documentation is huge, and sometimes I am lost where to start with it. So, I strongly recommend starting learning basic features, (like Simple Entity, List Entity) and understanding its fundamentals first. Next important thing is where you apply. And then you create suitable utterances and entities for intents. Finally, you can use additional features (like Pattern.any Entity, Hierarchical Entity, Composite Entity, Phrase Pattern, etc.)