#Event – #GlobalAICommunity Virtual Tour, April 8th. Let’s code a drone to follow faces! Using AI, Python, containers and more.

Buy Me A Coffee
Globa AI Community on Virtual Tour Logo

Hi !

On the 8th of April 2020 the Global AI Community is hosting a 30 hour live event across timezones in different languages. Mark the date in your calendar, subscribe to our YouTube channel and tune in.

We updated the agenda with an amazing set of great speakers, and super cool sessions. Take a look at the agenda and subscribe to the event here:

https://live.globalai.community/

And I realize that I forget to share details about my session, so here it is:

Let’s code a drone to follow faces! Using AI, Python, containers and more

You can control a drone using 20 lines of code. That’s the easy part. However, adding extra features like face or object detection and program the drone to follow and object or a face requires … another 20 lines of code!
During this workshop we will review how to connect to a drone, how to send and receive commands from the drone, how to read the camera video feed and how to apply AI on top of the camera feed to recognize objects or faces. We will use a simple house drone ($100) and Python. And, when we review some enterprise scenarios, we will use Azure Custom Vision in containers for some specific object recognition stories.
Let’s build this!

Happy coding!

Greetings

El Bruno

#Event – #GlobalAICommunity Virtual Tour, April 8th. A full virtual day with sessions around AI

Buy Me A Coffee
Globa AI Community on Virtual Tour Logo

Hi !

On the 8th of April 2020 the Global AI Community is hosting a 30 hour live event across timezones in different languages. Mark the date in your calendar, subscribe to our YouTube channel and tune in.

We updated the agenda with an amazing set of great speakers, and super cool sessions. Take a look at the agenda and subscribe to the event here:

https://live.globalai.community/

And as usual, a video is much better

Again, take a look at the agenda, say thanks to the organizers and we will see each other on April 8th !

Happy coding!

Greetings

El Bruno

#Podcast – NTN 42 Seguridad en Azure con @jc_quijano

display face landmarks in python using face recognition
display face landmarks in python using face recognition
Buy Me A Coffee

Buenas!

Vuelta a los podcasts y esta vez con Juan Quijano (@jc_quijano) en una conexión Madrid Toronto.

Juan es Microsoft Certified Trainer, Arquitecto de Soluciones en Azure y Consultor independiente en implantación de DevOps. Que es otra forma de decir que sabe, y mucho sobre Azure.

Por eso, esta es la perfecta excusa para hablar sobre Seguridad en Azure.

Ir a descargar

Happy coding!

Greetings

El Bruno

#Webinar – Drive real-world innovation with Azure AI

Hi!

The partner opportunity for AI is worth a whopping $118 billion,* and I want to help you make the most of it. Join me and SYNNEX for a webinar on Thursday, December 12, at 2 PM ET where I’ll show you how you can capitalize on Azure AI solutions that solve real-world problems for your customers.

I’ll cover:

  • What makes the AI opportunity so profitable for partners
  • How to create apps that see, hear, speak, and understand with solutions like Azure Cognitive Services and Power Apps
  • Steps for implementing AI and machine learning

Plus, I’ll provide demos of key solutions—you’ll see how easy it is to create a QnA bot with Azure Cognitive Services, or machine learning solutions with Power Apps. Register today!

*Tractica. Artificial Intelligence Software Market to Reach USD$89.8 Billion in Annual Worldwide Revenue by 2025. 2017.

Registration: http://bit.ly/2QFQP9Q

Greetings @

El Bruno

#Azure – Azure Open DataSets, an amazing friend for Azure #ML Studio (Preview)

Hi!

Time for a very interesting feature part of the Azure family: Azure Open Datasets. OK, when you read the name, you probably get 95% of the idea, however, let’s dig into the official definition (see references).

Azure Open Datasets are curated public datasets that you can use to add scenario-specific features to machine learning solutions for more accurate models. Open Datasets are in the cloud on Microsoft Azure and are integrated into Azure Machine Learning and readily available to Azure Databricks and Machine Learning Studio (classic). You can also access the datasets through APIs and use them in other products, such as Power BI and Azure Data Factory.

Datasets include public-domain data for weather, census, holidays, public safety, and location that help you train machine learning models and enrich predictive solutions. You can also share your public datasets on Azure Open Datasets.

This per-se is amazing, however this feature became useful when you start to work with the new amazing Azure Machine Learning Studio (Preview). Now in the [Assets / Datasets] section we can use:

  • Datasets from local files
  • Datasets from DataStore
  • Datasets from WebFiles
  • Datasets from the Open DataSet repository

And the last one is awesome because we can work with sample and free data like

All the datasets in the repository are optimized to be used in Machine Learning workflows. And, we have the chance to requests datasets or to submit and contribute with our own data. So Cool!

Happy coding!

Greetings @ Toronto

El Bruno

References

#Azure – Sending custom Telemetry and Event information from a #RaspberryPi device to #AzureIoT Central

Hi!

Azure IoT Central is one of the amazing services we can use on Azure. I was wondering how easy is to use a Raspberry Pi using Raspbian and Azure IoT and here is my experience.

Let’s assume we had a device up to date using Raspbian, our next step will be to create an Azure IoT Central application. The official step by step is the main resource for this

Create an
Azure IoT Central application (see references)

Once we have our application, we can quickly create a new Raspberry Pi device and use it. However, I’ll do an extra step, lessons learned as a handsome developer

Create a Device Template

Go to [Device Templates] and create a new template

azure iot central create new device template

For Raspberry Pi, I’ll name this [Raspberry Pi Dev]

azure iot central create new device template raspberry pi dev

So now, I can add a new real device, in the Devices section from the left menu

azure iot central raspberry pi dev add new real device

Once you create a new real device, is important to copy and save for later the connection information. To access this, go to the top right [Connect] button

azure iot central raspberry pi dev real device connect information

Almost there, there is an official tutorial that explain how to send random telemetry information with a Python script in a Raspberry Pi. I’ll use it as base for this scenario.

Connect a
Raspberry Pi to your Azure IoT Central application (Python) (see references)

For this demo, I’ll add a custom telemetry property and a custom event to the device. Since I won’t use the device to track temperature, accelerometer, and more, I think it make sense to track some custom information.

So, I’ll go back to my Device Template definition and I’ll add a new Telemetry, named [t1], with the following information.

azure iot central raspberry pi dev new telemetry information

And now, I can run a custom version of my script that will send new telemetry information, for [t1]. Sample in line 18

After a couple of minutes running the sample script, I can see the telemetry information for T1. In this view, I enabled [Temperature] and [T1] to display the timeline.

azure iot central raspberry pi dev real device dashboard telemetry

And, next step will be to add an event, which is also a very important uses case in Azure IoT. Back in the Device Template, I add a new event named [event1]

azure iot central raspberry pi dev new event information

And added some extra lines of code to send also an event between telemetry, Line 22

In the following image, we can see how the events appears in the timeline, and we can also get some extra details clicking on each event.

azure iot central raspberry pi dev real device dashboard telemetry and events

Very cool! Next steps will be to integrate this with some image recognition scenarios.

Happy Coding!

Greetings @ Burlington

El Bruno

References

#Event – Resources for the sessions about #DeepLearning and #CustomVision at the @ChicagoCodeCamp

Hi!

Another post-event post, this time with a big thanks to the team behind one of the most amazing event I’ve been this year: Chicago CodeCamp.

I had the chance to meet a lot of amazing people, to learn a lot during the sessions and also to visit the great city of Chicago.

As usual, now it’s time to share slides, code and more.

Deep Learning for Everyone? Challenge Accepted!

Let’s start with the Deep Learning resources


Demos Source Code: https://github.com/elbruno/events/tree/master/2019%2005%2011%20Chicago%20CodeCamp%20Deep%20Learning

Session: How a PoC at home can scale to Enterprise Level using Custom Vision APIs

And also the [How a PoC at home can scale to Enterprise Level using Custom Vision APIs] resources

Demos Source Code: https://github.com/elbruno/events/tree/master/2019%2005%2011%20Chicago%20CodeCamp%20CustomVision

And finally, some Machine Learning.Net, Deep Learning and Custom Vision resources:

My posts on Custom Vision and ONNX

  1. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  2. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  3. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
  4. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
  5. Can’t install Docker on Windows 10 Home, need Pro or Enterprise
  6. Running a Custom Vision project in a local Docker Container
  7. Analyzing images in a Console App using a Custom Vision project in a Docker Container
  8. Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
  9. Building the CustomVision.ai project in Docker in a RaspberryPi
  10. Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
  11. About ports, IPs and more to access a container hosted in a Raspberry Pi
  12. Average response times using a CustomVision.ai docker container in a RaspberryPi and a PC

Windows 10 and YOLOV2 for Object Detection Series

See you next one in Chicago for some Deep Learning fun!

Happy coding!

Greetings @ Toronto

El Bruno

#CustomVision – Es el momento de mover los proyectos de Custom Vision a #Azure!

Buenas !

Durante las ultimas semanas he escrito mucho sobre Custom Vision, ejemplos sobre como exportar modelos a formato ONNX o a imágenes para Docker; y luego utilizar estos modelos en apps de Consola, o en UWP Apps, inclusive con Docker en una Raspberry Pi. A este post lo tengo en borrador desde hace un tiempo, por lo que lo mejor sera que lo publique lo antes posible.

Si eres usuario de CustomVision.ai, seguramente has visto el siguiente mensaje cuando accedes al portal. El mismo nos avisa que el servicio dejara de estar disponible en modo preview / test el día 2019-03-19. Esto implica que si quieres seguir utilizando CV, debes mover tus proyectos a Azure.

Custom Vision moved to Azure

Una opción puede ser crear nuevamente los proyectos de CV, cargar las imágenes y hacer todo el proceso de etiquetado y entrenamiento desde cero. Esa opción es valida. Sin embargo, los nuevos proyectos tendrán nuevos IDs y también nuevas URLs para acceder a los HTTP EndPoints de los mismos. La otra opción es [mover a Azure] los proyectos de CV.

Lo primero que debemos hacer es crear un Custom Vision resource en una suscripción de Azure. Si conoces Azure estos son 2 clics, y muy fáciles.

azure custom vision resource

Podemos seguir utilizando un plan Free, con los siguientes parámetros:

  • Up to 2 projects
  • Limit of 5000 training images
  • 2 transactions per seconds
  • Limit of 10000 predictions per month

Custom Vision Azure Prices

Una vez creado el resource en Azure, debemos volver al portal de CustomVision.ai, seleccionar el proyecto que queremos migrar y en la sección Settings veremos una opcion [Move to Azure] en la esquina izquierda inferior.

Custom Vision move to Azure button

Teniendo en cuenta que solo podemos usar Proyectos de CV en una única región, por ahora, tendremos que completar los datos para mover el proyecto. Y listo! El proyecto de CV esta migrado a Azure 😀

Custom Vision move to Azure only in South Central

Happy Coding!

Greetings @ Toronto

El Bruno

Resources

  1. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  2. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  3. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
  4. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
  5. Can’t install Docker on Windows 10 Home, need Pro or Enterprise
  6. Running a Custom Vision project in a local Docker Container
  7. Analyzing images in a Console App using a Custom Vision project in a Docker Container
  8. Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
  9. Building the CustomVision.ai project in Docker in a RaspberryPi
  10. Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
  11. About ports, IPs and more to access a container hosted in a Raspberry Pi
  12. Average response times using a CustomVision.ai docker container in a RaspberryPi and a PC

Windows 10 and YOLOV2 for Object Detection Series

 

#CustomVision – It’s time to move your Custom Vision projects to #Azure!

Hi !

I’ve been writing a lot about Custom Vision, and how use and export CV models to ONNX or docker images to be used later in different types of scenarios. I got this post in draft mode, so it’s time to publish it.

If you are using CustomVision.ai, you probably notice the warning message about the service being moved from a preview / test stage on 2019-03-19. That’s mean that you need to move your CV projects to a valid Azure account if you want to use them.

Custom Vision moved to Azure

You may want to create and train again some cv projects, however you will get new project ids, new urls and you need to tag again all the images.

The 1st action here, is to create a Custom Vision resource in a valid Azure account. That’s a 2 click tutorial and it’s also very easy.

azure custom vision resource

There is also the option to continue working in a free mode scenario with the following parameters in the Free Instance:

  • Up to 2 projects
  • Limit of 5000 training images
  • 2 transactions per seconds
  • Limit of 10000 predictions per month

Custom Vision Azure Prices

Now we can go back to the Custom Vision.ai portal and select the project we want to migrate to Azure. In the Settings section, at the bottom left corner we have the [Move to Azure] option.

Custom Vision move to Azure button

Here we need to select the specific values of the resource we created before and that’s it! The Custom Vision project now is fully migrated to Azure 😀

Custom Vision move to Azure only in South Central

Happy Coding!

Greetings @ Toronto

El Bruno

Resources

  1. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  2. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  3. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
  4. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
  5. Can’t install Docker on Windows 10 Home, need Pro or Enterprise
  6. Running a Custom Vision project in a local Docker Container
  7. Analyzing images in a Console App using a Custom Vision project in a Docker Container
  8. Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
  9. Building the CustomVision.ai project in Docker in a RaspberryPi
  10. Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
  11. About ports, IPs and more to access a container hosted in a Raspberry Pi
  12. Average response times using a CustomVision.ai docker container in a RaspberryPi and a PC

Windows 10 and YOLOV2 for Object Detection Series

 

#Azure – Single Key for all Services in #CognitiveServices, that’s cool :D

Hi !

Quick Friday post. And an amazing one, because now we have the chance to create a single Key to be use around a bunch of Cognitive Services. That’s mean we don’t need to remember and store different keys for LUIS, Face Emotion, and more. A single key will cover most of these scenarios with 3 simple steps

 

More information in What’s New? A Single Key for Cognitive Services on Channel 9 by Noelle LaCharite

Happy coding!

Greetings @ Burlington

El Bruno