Everywhere, intelligence with cloud computing

A fresh perspective

December 19, 2022 Emerging Technologies No comments

It has been a while since I have written something on my blog. With COVID19 pandemic and all, whole world is affected, and I too lost my focus in technology at some point.

With this tiny note, I will be starting to share my new interesting learnings on Cloud Native and Multi cloud technologies.

May all the world will be peaceful without COVID-19.

Featured in Office 365 Developer Newsletter November 2019

November 5, 2019 Announcements, Microsoft, Office 365, Share Point No comments

As an Office 365 Developer and organizer of Office 365 developer events in local community, I have got an opportunity to be featured in November 2019 newsletter.

image

image

image

Image Classification vs Pattern Recognition vs Object Detection vs Object Tracking–A Primer

December 24, 2018 Algorithms, Artificial Intelligence(AI), Computer Vision, Emerging Technologies, Image Classification, Image Recognition, Object Based Image Analysis, Object Classification, Object Detection, Object Recognition, Object Tracking, Pixel Based Image Analysis No comments

It is a common question that has been asked in all Artificial Intelligence Conference or Discussion Forums. Based on my knowledge, I thought of answering some of these questions:

1.) Image Classification (also called Image Recognition): is the process of creating a thematic image where each pixel is assigned a number representing a class / tag (this also includes ‘unclassified’. In a persons image the classes can be “complexion”, “gender”, “ethnicity”, “representational names”, etc.  

image

2.) Pattern Recognition (also called Object Recognition):  Identifying/recognising the things or objects in an image. This answers the query – what objects or things are depicted in this image? For example, if you are searching for farm lands in an areal image, and other objects such as tractors, sheep’s, cattle’s etc.  There are two classifiers you can familiarize for pattern recognition.  Pixel based classification and object based classification.

  • Pixel based classification: Most of the image analysis algorithms you would encounter would be based on pixel based, However, there is a major drawback for pixel based algorithms that they do not take contextual information in to consideration. Context is how these pixel/object is associated to its environment(a relationship).
  • Object based classification: To overcome this limitation of pixel based classification, we could use Object based image analysis(OBIA) to divide images in to meaningful image-objects and evaluating their characteristics in spatial, spectral and temporal aspects.

Here is how both classification will look like one vs the other:

image

3.) Object Detection: is another confusing terminology, Object Recognition was able to recognize – what type of object it is? Now object detection answers the query – where is this specific object?  Object detection is based on the point of interest of any given image; for instance electronic devices such as laptop/mobile phone in picture and recognition talked about the specific information about electronic devices, like name, type and other characteristic of particular interest point.

image

4.) Object Tracking: Done in motion pictures like in animated gifs or videos, we want to track how an object is moving, where is it going, or its speed. For example: vehicle tracking in a traffic camera system.Or a Realtime object or people tracking like in Object Detection figure.

image
image

End of Microsoft Virtual Academy–Welcome to Microsoft Learn

December 24, 2018 Community, KnowledgeBase, Microsoft Learn, Microsoft Learning, Microsoft Virtual Academy, MVA No comments

In October, During Ignite 2018 Conference in Orlando, Microsoft announced the availability of new free interactive and sandbox based learning platform called  “Microsoft Learn”, and during these months Microsoft has been adding more and more specific contents for different roles such as Azure Developer, Azure Administrator, Azure Architect etc.

There is a specific learning path for all roles and you can experience different role based contents being aligned in each bucket.

Soon you will be seeing more specific contents for Power BI, Power Apps, Microsoft Flow and Dynamics 365 etc being added in to this platform.

tempsnip

Benefits of Microsoft Learn:

  • No Fee: This platform is free, you are going to use a free azure sandbox for playing around with Hands-on learning.
  • Points for Learning: XP points for each sections you complete and you have different levels. I am on Level 7 on my learning curve.
  • Specific Learning paths leads you to successful Microsoft certification exams. a learning path tailored to today’s developer and technology masterminds and designed to prepare you for industry-recognized Microsoft certifications.

 image

End of Microsoft Virtual Academy(MVA)

Microsoft Virtual Academy has been a free platform for learning about Microsoft technologies and earn completion certificates etc. Since we have the new interactive learning experience available through Microsoft Learn, Microsoft has decided to phase out Microsoft Virtual Academy and we have received an email confirming the same.

Planned complete retirement of MVA by Jan 29’ 2019.

Excerpt from the email received:

To simplify your tech training journey, we are consolidating our learning resources and retiring Microsoft Virtual Academy in phases, beginning on January 31, 2019. Complete site retirement is scheduled for later in 2019. Check your MVA Dashboard frequently for courses you have started that are retiring. To earn your certificates of completion, be sure to finish any courses by January 31, 2019.


image

Enough said based on my experience I found Microsoft Learn is the new age learning platform providing gamified experience through the learning curve and earning XP points, unlocking achievements gives you a level of satisfaction. Smile

So join Microsoft Learn today.

image

Azure Cognitive Services–Experience Image Recognition using Custom Vision (Build an Harrison Ford Classifier)

December 23, 2018 Algorithms, Artificial Intelligence(AI), Azure AI, Cognitive Services, Compuer Vision Service, Computer Vision API, Custom Vision API, Custom Vision Service, Emerging Technologies, Machine Learning(ML) No comments

Custom Vision Service as part of Azure Cognitive Services landscape of pretrained API services, provides you an ability to customize the state-of-the-art Computer Vision models for your specific use case.

Using custom vision service you can upload set of images of your choice and categorize them accordingly using tags/categories and automatically train the image recognition classifiers to learn from these images and come up with image recognition predictions when you supply an input image. Later consume this service as an API in your existing applications.

image

For example:  Here is how an image of Hollywood Actor – Harrison ford being accurately predicted by the custom model through training using a series of pictures of Harrison Ford through different ages and shapes.

I build this sample during Global AI Bootcamp Letterkenny– Hands-on Labs and will take you further through this article. Harrison Ford is my all-time favorite actor.

image

Another example, Harrison Ford was one among 3 in a photo. Here is how the results would look like.

image

Here is how Harrison Ford’s son’s picture is being predicted as Harrison Ford, due to similar facial characteristics. |f we further train this model, we can improve its capabilities to come up with accurate predictions.

image

Now let us see, how it was implemented.

In this article I am going to use a set of Harrison Ford images found on Google Images and then upload them to Custom Vision service like below. For more accuracy, I tried to collect images of Harrison Ford through different stages of his life, so that computer vision model could evolve to predict more accurate results.

image

Getting Started with Custom Vision:

The Azure Custom Vision API is a cognitive service that lets you build, deploy and improve custom image classifiers. An image classifier is an AI service that sorts images into classes (tags) according to certain characteristics. Unlike the Computer Vision service, Custom Vision allows you to create your own classifications. The Custom Vision service uses a machine-learning algorithm to classify images.

Classification and object detection

Custom Vision functionality can be divided into two features. Image classification assigns a distribution of classifications to each image. Object detection is similar, but it also returns the coordinates in the image where the applied tags can be found.

To get started with our example,  first you need to have a Microsoft Account and Register/Login to https://www.customvision.ai

There going to be five steps of activities we are going to do:

1. Setup a Custom Vision Project

Create a new Project by selecting ‘New Project’ button

image

Specify the naming as the followed:

  • Name: HarrisonFordClassifier
  • Description: HarrisonFordClassifier
  • Resource Group: Leave it default to ‘Limited Trial’
  • Project Types: Classification
  • Classification Types: Multi Label (this is essential, we are going to add multiple tags per image in this example: for say ‘Actor’, ‘Person’ and ‘Harrison Ford’
  • Domain: General (for now)

image

2. Upload the Images

a.) Prepare Images

I have gathered a set of images you can download it from here, and extract the HF-Demo-Images.zip in to a folder of your choice.

There are two folders in it  first folder(harrisonford) contains all reference images for training the model and second folder(hf-quicktest) contains all the quick test images we are going to use for evaluating the model.

image

b.) Create Tags

Select ‘+’ icon to create a new tag and create the following tags

  • Actor
  • Hollywood
  • Harrison Ford
  • Person
  • Male

image

Enter Tag Name and click on ‘Save

image

c.) Upload Images

Now that we created all the tags, lets upload the images and tag them with respective tags.

Click on ‘Add Images’ button and select the images from “harrisonford” folder to upload.

image

image


d.) Assign Tags

Now specify the associated tags in My Tags section, selecting from the drop down

image

Then click on Upload

image

Have a review of the images uploaded

image

3. Train

Now let us train the model by selecting the green train button on top right hand side of the page

image

This initiates the first automatic training(Iteration 1) based on the tags you assigned and images associated with it.

image

Once that step is completed let us review the output of the training.

It shows precision and Recall of 100% indicates our image classification model is trained now to provide Precision of 100% and Recall of 100%.

PS: Recall means out of the tags which should be predicted correctly, what percentage did our model correctly find?

image

4. Evaluate the Model

Now that our classifier is trained, let us evaluate the accuracy. For that we are going to use the sample images from “hf-quicktest” folder.

a.) First click on Quick Test button on top – image

b.) Select a local image or select an image URL

image

image

image

Lets try another image

image

image

Next let us try to upload an image of Ben Ford (Harrison Ford’s son)

image

5. Active Learning

Now that we have a couple of accurate predictions, Active Learning involves training the model again from the prediction samples we used. This would make the model evolve to provide us more accurate predictions, for example we correcting the model as it identified that Ben Ford also as Harrison Ford based on similar facial features. In real world, he is a different entity other than his father.

Ben Ford is a Chef by profession. So I am going to upload some of his pictures and tag them as Ben Ford. Also couple of images of both father and son together, and then initiate the training again. Hope they would not feel agitated.

image

image

Now if you look at training performance, Precision and Recall values came down a bit, we can realize it is because we have two persons being tagged with some common tags etc.

image

Let us do a Quick Test with the previous image of Ben Ford again. voilà!, we have some accurate prediction.

image

Similarly, we can repurpose some of the previous prediction images from Predictions tab and add them with right Tags. Then retrain the model again to evolve the model.

image

image

image

The End:

Now that you have learned how you can train Custom Vision API with set of images and retrain them again for more accuracy. Once your training is completed and you are happy with the performance, you can integrate the logic in to your existing apps using Custom Vision REST APIs. You can follow the HOL that covers the integration topic here.

Custom Vision Services provides you state-of-the-art Classification and object detection capabilities to customize it for your specific need with quick and easy steps. This help you reduce your time to market and increase ROI (Return of Investment) for your product lines or ideas.

Start learning today using the below reference links.

References:

Disclaimer: All the images referenced in this article are available on the public domain and there is no way any private images are been included in this examples. We respect Harrison Ford and his family privacy, this article is just an attempt to prove the capabilities of Azure Custom Vision Services, no way intended to insult or invade Mr.Harrison Ford’s privacy.I am a big fan of you sir.

Azure DevOps–Community Launch-Letterkenny (08-January’ 2019)

December 21, 2018 .NET, .NET Core, .NET Framework, Announcements, Azure, Azure DevOps, Azure DevOps Server, Azure DevOps Services, Community, LKMUG, Microsoft No comments

Inviting you all to Azure DevOps Community Launch in Letterkenny on 08th Jan 2019. Few months back Microsoft Visual Studio Team Services has been rebranded as Azure DevOps.

Azure DevOps is now a suite of separate but integrated services for managing software projects, source control, build and release management and automation testing to enhance your productivity and team performance in whatever development and deployment environment you choose.

Martin Woodward: The Principal GPM for @AzureDevOps and Vice-President of the .NET Foundation would be joining us in Letterkenny for this event.

To know more about Martin Woodward:

highres_476929143

What we’ll cover:
 Introduction to Azure DevOps
 Azure Pipelines: Fully managed CI/CD platform that works with any language, platform, and cloud
 Azure Repos: Source code repositories (Git/TFVC)
 Azure Test Plans: Manual and Automated Testing
 Azure Boards: Plan, track, and discuss work across your teams
 Azure Artifacts: Package management

Additionally, We’ll also cover how to use Azure Pipelines for Continuous builds with your GitHub projects.

If you are interested to learn how to plan smarter, collaborate better and ship faster, sign up now and RSVP/Share it: https://www.meetup.com/lk-mug/events/255764767/ 

Please follow us on: