Earlier this week we talked about getting up and running on Azure to start exploring the new machine learning service. From here, you can really start to dig in and try out the capabilities of a cloud ML and put predictive analytics into practice.
Below you’ll find several more video tutorials that help you learn your way around the service. Check them out, and let us know what predictions you uncover or big ideas you solve.
- Getting and Saving Data in Azure ML Studio: Data Access is the first step of data science workflow. Azure Machine Learning supports numerous ways to connect to your data. This video illustrates several methods of data ingress in Azure Machine Learning.
- Pre-processing data in Azure ML Studio: Data preprocessing is the next step in data science workflow and general data analysis projects. This video illustrates the commonly used modules for cleaning and transforming data in Azure Machine Learning
- R in Azure ML Studio: Azure Machine Learning supports R. You can bring in your existing R codes in to Azure Machine Learning and run it in the same experiment with provided learners and publish this as web service via Azure Machine Learning. This video illustrates how to incorporate your R code in ML studio
- Predictive Modeling with Azure ML: Azure Machine Learning features a pallets of modules to build a predictive model, including state of the art ML algorithms such as Scalable boosted decision trees, Bayesian Recommendation systems, Deep Neural Networks and Decision Jungles developed at Microsoft Research. This video walks through steps to building, scoring and evaluating a predictive model in Azure Machine Learning
- Deploying a Predictive Model as a Service – Part 1: This video walks through creating a Web service graph for a predictive model and putting the predictive model into staging, using the Azure Machine Learning API service
- Deploying a Predictive Model as a Service – Part 2: Azure Machine Learning enables you to put staging service into production via the Azure Management portal. This video walks through putting the predictive model staging service into production
As a reminder, there are a ton of resources you can use to continue your learning: