Artificial Intelligence and Machine Learning are the latest in a long line of tech-worthy buzzwords.

Given the hype, it is often difficult to separate the technology from the marketing. Entrepreneurs need to understand what the technology actually does and the problems it solves.

In this talk by Seth Juarez, learn how AI and ML work, the kinds of problems it solves, and what the implications of its use are for your startup.

Armed with this practical knowledge, you’ll be able to make better decisions about where to start and how you can use machine learning and AI in your businesses.

For more information head over to https://aka.ms/aidevresources

Machine learning is a trend that you cannot miss out on when developing an Android mobile app for the digital era

Remember Tony Stark’s assistant Jarvis? or HAL? Or the computer on Star Trek?

With machine learning taking off the way it has, this is not science fiction anymore.

You very likely have a personal assistant very similar to Jarvis in real life.

Siri/Cortana/Alexa/Google understands you, uses neural networks to study your patterns, and enables personalized settings just for you.

The mobile app market is fast-evolving. When the statement “there is an app for everything” made by Apple’s then-CEO came forth, it was not taken very seriously. However, the way the mobile apps are being consumed, the new truth is that not only do you have an app for […]

Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself.

But what exactly does ML actually look like?

In part one of Machine Learning Zero to Hero, AI Advocate Laurence Moroney walks through a basic Hello World example of building an ML model, introducing ideas which we’ll apply in later episodes to a more interesting problem: computer vision.

Try this code out for yourself in the Hello World of Machine Learning: https://goo.gle/2Zp2ZF3

In this article, explore how Reddit has been using machine learning and what its plans are for the future.

Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their current ML projects and future efforts in the space. Based on a talk given by Anand […]

ML.NET allows .NET developers to easily build and also consume machine learning models in their NET applications.

In this episode, Bri Achtman joins Rich to show off some really interesting scenarios that ML.NET and its family of tools enables. They talk about training models, AutoML, the ML.NET CLI, and even a Visual Studio Extension for training models!

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Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations.

This has led to a rallying cry for model interpretability.

Yet the concept of interpretability remains nebulous, such that researchers and tool designers lack actionable guidelines for how to incorporate interpretability into models and accompanying tools.

This panel discussion hosted by Microsoft Research brings together experts on visualization, machine learning and human interaction to present their views as well as discuss these complicated issues.

Did you know that you can now train machine learning models with Azure ML once and deploy them in the Cloud (AKS/ACI) and on the edge (Azure IoT Edge) seamlessly thanks to ONNX Runtime inference engine.

In this new episode of the IoT Show, learn about the ONNX Runtime, the Microsoft built inference engine for ONNX models – its cross platform, cross training frameworks and op-par or better performance than existing inference engines.
From the description:
We will show how to train and containerize a machine learning model using Azure Machine Learning then deploy the trained model to a container service in the cloud and to an Azure IoT Edge device with IoT Edge across different HW platform – Intel, NVIDIA and Qualcomm.