Bruno Capuano

Information & Communications Technology

Artificial Intelligence Machine Learning and Artificial Intelligence Machine Learning Mixed Reality Microsoft MVP

Toronto, Ontario, Canada

Bruno Capuano

Microsoft AI MVP

Bruno Capuano leads Innovation at Avanade Canada and has been a Microsoft MVP for the past 14 years. He is an active Microsoft Technologies Community participant, a writer, and a frequent speaker at local and international technology conferences.
Bruno also like to hack new technologies, play the guitar and run. Run a lot, which suddenly started after becoming the dad of the 2 best sons ever.
You can contact him via twitter (@elbruno) or on his blog: http://www.elbruno.com

Current sessions

TinyML and SkySharks 🦈🦈🦈

Sharks are amazing animals, so let’s start there. In this session we will also review Machine Learning concepts, and we will focus in TinyML: ML technologies and applications capable of performing on-device sensor at extremely low power.
We will cover hardware, algorithms and software, and we will build and deploy our own TinyML model to detect flying sharks.
Let’s build this!


Everyday Artificial Intelligence!

Have you ever been in a situation where you were going to start a presentation and you feel / know that this will be boring? If so, this session will help you.
Have you read a lot about Artificial Intelligence, and it seems widely amazing, but ... you still don't really feel it? If so, this session will help you.
During this session Bruno will be sharing a set of 5 minutes demos which are probably part of you everyday tasks. And yes, they are all based on AI!


Diversity is more, much more! Living in tech as a Latino who can’t dance

I was born and raised in Argentina, then I lived +11 years in Spain and +3 years ago I moved to Canada. During the past 15 years I was an outsider, and this also impacted my professional life. I was lucky enough to travel for 5 years working in Europe, and I learned a lot on how my latino background impacted my professional career.
This session is mostly sharing what I’ve learned during these past years. How I learned to get the most from sentences like “You have a very strong accent; I don’t think you will fit this meeting. These are Senior Executives” or “Wow, your accent is amazing, our leadership team will love it!”. In both scenarios, I manage to move from frustration or surprise to a let’s focus on our business.
And it’s not only about language and accent; family, culture and even food and drinks are a part of the story. Espero que nos veamos en la sesion!


How a PoC at home can scale to Enterprise Level using Custom Vision APIs (v2.0))

It all started with a DIY project to use Computer Vision for security cameras at home. A custom Machine Learning model is the core component used to analyze pictures to detect people, animals and more in a house environment. The AI processing is performed at the edge, in dedicated hardware and the collected information is stored in the cloud.
The same idea can be applied to several CCTV scenarios, like parking lots, train stations, malls and more. However, moving this into enterprise scale brings a set of challenges, which are going to be described and explained in this session.
In this new version of the session, we will start from scratch and create a complete “Parking Garage Open Space Tracker” solution with live devices and live cars (small ones, of course)
Let’s code this.


Let’s code a drone to follow faces syncing everything with Azure IoT

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 IoT to sync the drone information in IoT mode.
Let’s build this!


Let’s build an Anomaly detector system with a few lines of code and without code at all!

Detect anomalies is a common scenario which affect dozens of industries. From analysis of Power Consumption, analysis of Medical Data and even analysis of personal information, anomalies can be detected based on historical data.
During this workshop we will code a complete system to detect anomalies, we will train a create a model and later use this model with new data to identify anomaly. Later in the workshop we will review a new set of options to create an Anomaly Detection System without a line of code!
Let’s build this!


Let’s build a room occupancy detector system using BLE and RaspberryPi.

Tracking people based on BLE and smartphones or beacons is a popular solution. However, how about replace some core components of this solution using Power Automate Flows, Power Platform Apps and Power BI. That’s seems a nice challenge!

During this session we will review how to create a simple BLE tracker using a Raspberry Pi, with some code! And then how to use Flows to orchestrate the people movements around a space, Power Apps to display in real-time rooms occupancy and PowerBI to analyze scenarios and predict future occupancy charges.

Let’s build this!


Getting Started with Machine Learning.Net and Auto ML

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.
And if you are a .Net developer, we will cover the basis of Machine Learning.Net, a complete ML framework to work with C#, F# or any other .Net Core language.


Coding4Fun – Detectando mascarillas, temperatura corporal, Social Distancing y más con Python, C#, M

En sesiones anteriores compartí ejemplos fáciles, por ejemplo, como controlar un dron utilizando 20 líneas de código. Las posibilidades que tenemos a nuestro alcance hoy con Computer Vision, Azure, IoT y otras tecnologías nos permiten dar un paso más adelante y comenzar a pensar en soluciones para escenarios reales.
Durante esta sesión utilizaremos C# y Python para la creación y consumo de modelos de Machine Learning. Aprovecharemos las capacidades de un dispositivo como Raspberry Pi para tener estas funcionalidades en lugares remotos y conectados. Y finalmente, utilizaremos varias capacidades de Azure para coordinar y orquestar los flujos de información.
Happy coding!


Lessons Learned creating a multiplatform AI project for Azure Kinect and Hololens 2

It all started with a 10000 kms conversation between 2 friends about how easy is to port Mixed Reality projects between platforms. So, we choose Azure Kinect and Hololens 2 as the platforms to test this out. To make this more challenging, we also decided to place custom holograms in those different platforms based on some cool Image Recognition scenarios (custom Artificial Intelligence rocks!)

During this session we will review how to use MRTK, Azure Kinect SDK, Computer Vision, and other cool technologies to make this happen. And, of course, be aware that this session is full of code, hardware and demos, do not expect a lot of slides.

Let’s code / build this.


Let's rock some AI with Azure Machine Learning

Machine learning is a complex subject that requires a great deal of advanced math, software development skills, hardware setup and much more. Simple tasks like setup a ML environment can be very time consuming.

That's why AzureML is a great solution to focus on the important task: Machine Learning. In this session we will review how to train and deploy machine learning models using different techniques, no code approach using the drag-and-drop designer, programming with Jupiter Notebooks and also with Automated Machine Learning.

Let's rock this !


Let’s perform Anomaly Detection using ML.Net on a drone flying data from Azure IoT

You can control a drone using 20 lines of code. That’s the easy part. Sending the drone telemetry to Azure IoT is a little tricky, however, it requires another 20 lines of code. And when all the information is on Azure IoT, we can perform Anomaly Detection on the drone telemetry using ML.Net on the cloud.
We will use a simple house drone ($100), Python and C#. And, besides the drone telemetry analysis, I’ll share additional enterprise scenarios.
Let’s build this!

1st part of the track is about how to create an SDK to control the drone. 2nd part is how to send the telemetry to Azure IoT. 3rd part is how to analyze this telemetry using ML.Net. 4th part is to share real scenarios on where to apply this. I’m planning to spend +60% of the session on steps 3 and 4.
For each event, I prepare a custom demo for the 2nd part :D


Past and future events

Caribbean Developers Conference

2 Oct - 6 Oct 2019
Punta Cana, La Altagracia, Dominican Republic

Global Azure Bootcamp 2019 Mississauga

27 Apr 2019
Mississauga, Ontario, Canada

MVPDays Online January 2019

30 Jan 2019
Calgary, Alberta, Canada

CodeMash 2019

7 Jan - 11 Jan 2019
Sandusky, Ohio, United States

Global AI Bootcamp - Toronto

15 Dec 2018
Mississauga, Ontario, Canada