Henk Boelman

Build your own A.I. powered robot

The need to jump on the AI track is more relevant than ever. With the commodity of hardware and the computing power of the Cloud, Artificial Intelligence is at our fingertips and waiting to be implemented in new innovative solutions.

This workshop gives a quick start in the world of AI by combining Windows IoT, a Raspberry Pi with some sensors and the power of Microsoft Cognitive Services.
You will learn how a Raspberry Pi works, dive into topics like face identification, language understanding and how to train your own vision model.

The workshop is very hands-on and at the end you have made a robot that can talk and uses its vision and ears to interact with its surroundings. The bot will run on a Raspberry Pi powered by Windows IoT. The bot will be able to see, hear and talk using different Cognitive Services.

What you will learn:
• Windows IoT
• Raspberry PI with sensors
• Face detection API
• Language Understanding
• Custom Vision

After the workshop, you know what Cognitive Services are and have hands-on experience implementing them in a program. You can connect sensors to the Raspberry and use their capabilities in your programs.

Who should attend:

This workshop is for you if you:
• Want to learn what all the different Cognitive Service are and how to use them.
• Learn about the Raspberry Pi works and write software for it.
• Play around with hardware and like connecting wires and sensors

What to bring:
Bring your laptop running the latest Windows 10 and Visual Studio 2017. You will need an Azure subscription to activate the Cognitive Services APIs, if you don’t have one please create one before the workshop.
Read this before attending: https://github.com/hnky/Workshop-AI-Pre

The AI revolution is coming, and you can be part of it!

1 day or 2 day workshop
15-20 people

A.I. on the Microsoft Stack

The need to jump on the AI track is more relevant than ever. With the commodity of hardware and the computing power of the Azure Cloud, Artificial Intelligence is at our fingertips and waiting to be implemented in new innovative solutions.

In this session we do a deep dive into the A.I. stack that Microsoft has to offer. We start with pre-trained Cognitive services like Computer Vision, Anomaly Detection and Speaker recognition. And move on to the more advanced tooling like Machine Learning Studio and Azure Machine Learning Services.

At the end of this session you have a good grasp of the technological building blocks that make AI happen. Just waiting to be used in your own projects afterwards.

The AI revolution is coming and you can be part of it!

Getting started with Azure Machine Learning services

With machine learning becoming more and more an engineering problem the need to track, work together and easily deploy ML experiments with integrated CI/CD tooling is becoming more relevant then ever.

In this session we take a deep-dive into Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy, and manage models. We zoom into the building blocks provided and show, through some demos, how to use them.

At the end of this session you have a good grasp of the technological building blocks of Azure machine learning services. Just waiting to be used in your own projects afterwards.

AI in the battle against fakes.

Counterfeit products have been a longstanding and growing pain for companies. In addition to impacting company revenue, they damage brand reputation and customer confidence. Our company was asked to build a solution for a global electronics brand that can identify counterfeit products by just taking one picture on a smartphone.

In this session we zoom into the building blocks that make this AI solution work. We’ll find out that there is much more to it than just training a convolutional neural network. We look at challenges like how to manage and monitor the AI model and how to build and improve the model in a way that fits your DevOps production chain.

Learn how we used Azure Functions, Cosmos DB and Docker to build a solid foundation. See how we used Azure Machine Learning service to train the models. And find out how we used Azure DevOps to control, build and deploy this state-of-the-art solution.

AI used to only be in the hands of data science PhDs. Now any developer can integrate this exciting technology into their next project. Join us to find out how.

DevOps and Machine Learning

With machine learning becoming more and more an engineering problem the need to track, work together and easily deploy ML experiments with integrated CI/CD tooling is becoming more relevant than ever.

In this session we take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.

At the end of this session you have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.