I tested a few approaches and showed how to explain the model using Lime, how to measure feature importance, fight against class imbalance, and few other related topics. I recently performed a certification on Azure and wanted to test Azure Auto ML features on a simple example. So, I thought to use the previous examples and see how Azure AutoML simplifies the whole process. To use Auto ML from Azure, you need an Azure account of course. Then you can perform quite a few simple steps to create a machine learning workspace where your experiments can be registered.
Machine learning has made a significant shift from academia to industry in the last decade. The combination of large datasets, computing resources and significant investments have allowed researchers to push state-of-the-art results on most machine learning benchmarks. However, we are missing the fundamental tools to manage machine learning teams and processes. Over the past year, we conducted short interviews with over 200 data scientists from a variety of companies and research institutes. We asked them about their processes and their team dynamics.
My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.