Andy Cross is a co-founder of Elastacloud. An international speaker, Andy has lead teams building the largest AI projects and deployments, hosted on Azure. His passion for embedded software and high performance compute clusters gives him a unique insight into a sphere of computation from the very small and resource constrained to the massively scalable, limitless potential of the cloud. He is an Azure Insider, co-founder of the UK London Azure User Group, an Azure Most Valuable Professional and a Microsoft Regional Director. You can tweet Andy at @andyelastacloud
What does a mature IoT Solution look like? Is it enough to just connect up to the cloud and go? What about my devices that can't speak Cloudy? How do I understand and control what this costs me? What about the promised AI that lives in the Cloud??
In this session, Andy introduces and explores the role of Edge devices in a modern IoT architecture and can be made intelligent.
There will be demos using Azure IoT tools, code, Cloud resources and simulated IoT Edge devices.
Preferred duration 60 minutes
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
In this Session, Andy will show not only the core of ML.NET but best practices around Azure Data Lake and data in general when using .NET
Andy Cross, Director of Elastacloud, Microsoft Regional Director, Azure MVP and all round good guy, gives a session on how to successfully build or transform a business using AI technologies.
Over the last years, Elastacloud have delivered analytics projects to a variety of customers. The greatest challenges around AI are both technical and organisational. The existing landscape of process and strategy doesn't solve these challenges in combination, and the gap between causes friction and the failure of AI projects.
When modelling the outcome of actions that were informed by AI, possibly enacted by AI, the standard risk modelling approaches need to be transformed to include a factor that can change over time to represent the effectiveness of the AI solutions. Given that we should accept errors as part of the AI solution, and that errors are reinforcing of better future decisions, we need to project risk as a decreasing vector over time.