Artificial Intelligence Machine Learning and Artificial Intelligence Machine Learning Mixed Reality Microsoft MVP
Toronto, Ontario, Canada
💬 On 2021 I joined Microsoft as Senior Cloud Advocate 🥑. Before this I was a Microsoft MVP for 14 years, since 2008. The past 4 years I was awarded in the Artificial Intelligence category.
🌱 Previous to Microsoft, I was leading the Innovation Team for Avanade Canada, which means I spent much part of my time trying to figure our the best way to connect real Business with cool technologies, and engage my colleagues to perform smart and fun activities like participate in hackathons, coordinate hackathons and Innovation Contests, support tech meetings, and more!
🤖 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
En 2021 comenc'e a trabajar como Sr. Cloud Advocate en Microsoft. antes fuí reconicido como Microsoft MVP de Microsoft durante 14 años en diferentes categorías como Intelligencia Artificial, DevOps y Windows Developer.
I’m not a Unity3D Developer, however, I like a challenge from time to time.
So, this time, I pickup a Digital Twin WebGL project created with Unity3D, and I migrated the experience to be a Mixed Reality experience, focused on Hololens 2.
Besides the platform change, a new interaction and goal on the experience was required.
During this session, I’ll share my lessons learned about this project. Besides the standard technical ones, I’ll also share user experience and deep platform lessons.
In other words: I hope that these experiences can help you save some time !
The backend of the Digital Twin project is based on Azure services, with Azure IoT as the main component.
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!
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!
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.
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!
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!
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.
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.
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.
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 !
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
Winter is here and my backyard squirrels friends still come every day to get some food, snow is not a problem for them.
In this session we will review the basic concepts of Azure IoT and we will create a simple animal detector. When an animal is detected, the device will that will play video and music. And it will also send telemetry to Azure IoT to control the automatic feeder.
We'll share a few implementation options, so you can get some fun ideas and walk away with a plan to start creating your own masterpiece.
- Learn how to create ready to use IoT application.
- Learn how to develop for low powered devices with accessible sensors.
- Use analytics capabilities to analyze historical data and correlate telemetry from devices.
- Network with other professionals interested in Azure IoT
I'm always in charge. Cycling through Copilot suggestions and manually editing the suggested code is an amazing flow. What I really like is that GitHub Copilot adapts to my own code style.
2 Oct 2019 - 6 Oct 2019
Punta Cana, La Altagracia, Dominican Republic
27 Apr 2019
Mississauga, Ontario, Canada
30 Jan 2019
Calgary, Alberta, Canada
7 Jan 2019 - 11 Jan 2019
Sandusky, Ohio, United States
15 Dec 2018
Mississauga, Ontario, Canada