Workshop Description: The software engineering life cycle is well established in the industry, from requirements, to design, build, deploy and maintain. Now it’s time to apply this to our machine learning projects. Azure Machine Learning is a platform for developing and deploying your machine learning models on Azure.
Use your favourite frameworks, libraries and tools to build and deploy your machine learning experiments with the help of the cloud.
In this workshop, we will look at the lifecycle of your projects: from data to model and model to consumption in the real world. You can follow along with code samples I will share, as well as using these examples to help you replicate this workshop with your own projects after the event.
What you need to prepare before the workshop:
* You will need to bring a laptop with a modern web browser
* Have access to an Azure Subscription. If you do not have one you can sign up here for a free trial: https://azure.microsoft.com/en-gb/free/
( Resources will be available on GitHub during the session
Amy Boyd is a Cloud Advocate at Microsoft, having obtained a degree in Computer Science, completing a research project in Natural Language Processing/Machine Learning and an internship at Microsoft’s search engine, Bing. Amy is passionate about Data Science and Machine Learning and her roles at Microsoft have allowed her to work on many different areas of data science (visualisation, ML, big data, IoT) as well as working on projects with customers across the globe. Her role as a Cloud Developer Advocate is to help developers to engage with Microsoft around Microsoft Azure and specifically the Microsoft AI Platform by providing content, learnings, and sample code. You will find her sharing her content and learnings online and at in-person events.