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.
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!
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
For
Raspberry Pi, I’ll name this [Raspberry Pi Dev]
So now, I
can add a new real device, in the Devices section from the left menu
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
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.
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.
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]
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.
Very cool!
Next steps will be to integrate this with some image recognition scenarios.
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.
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.
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
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.
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 😀
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.
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.
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
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.
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 😀
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
So my friends from the Mississauga .Net User Group (link) were kind enough to invite me to host a session on November 22th, in TEK Systems in Mississauga. I’ll share some of the updates on ML.Net, currently in version 0.6 and some other very cool stuff around Microsoft and AI.
Machine Learning has moved out of the lab and into production systems. Understanding how to work with this technology is one of the essential skills for developers today. In this session, you will learn the basics of machine learning, how to use existing models and services in your apps, and how to get started with creating your own simple models. In other words, if you are a .Net developer, this session is for you. We will cover the basis of Machine Learning.Net, a complete ML framework to work with C#, F# or any other .Net Core language.
So my friends from the Azure Group Meetup (link) were kind enough to invite to host a session on November 14th, in the best place ever: Avanade Toronto office! So I’ll match an Innovation Day with a session or Artificial Intelligence in Azure.
Join us for a review of AI, Machine Learning and Cognitive Services in Microsoft Azure. In this session we will discuss some of the most popular tools and frameworks available for AI in Azure: Cognitive Services, Azure Machine Learning, and the Azure Bot Service.
You will get to see lots of code and demos of some real world applications using the various different Cognitive services including vision, speech, and language.
During the past Faculty Summit 2018, one of the exhibitions presented some details of the future [Project Kinect for Azure]. The following video gives a demonstration of the quality of the depth sensor in near and remote mode. In both demonstrations you can see that the same is much more powerful than the sensors we know.
And a special focus at 01:50 in the video, when a reference is made that this technology will be used in the next version of Hololens. The tracking options for the new device will be impressive.
