linkAzure ML Demo Agenda

linkWhat will be covered

• Introduction to AzureML - What it is, what it isn't, why it is useful
• Introductions to the resources for this course, our github, MSLearn Modules
• Setting up cloud based resources and workspaces in AzureML (New Version - Not classic)
• Setting up your cloud based compute environment (with SSH Access)
• Setting up a compute cluster (Seperate compute for 'number crunching')
• BUGFIX Updating scikit learn from the CLI (small workaround to deal with bug.. will explain)
• Cloning the MNIST dataset and AzureML Tutorials into your workspace (Sort of like cloning the git repo but within Azure)
• Using Logistic Regression to train a model using the imprted jupyter notebook
• See the results of this trained model (Note, we are creating a model to read handwritten digits)
• Deploy the model to ACI (An active containerised instance of your model, that you can test data on)