AZURE MACHINE LEARNING

Day 1

Day 1

Brief information about Azure Machine Learning

What is Azure Machine Learning?

A cloud-based environment You can use to train, deploy, automate, manage and track machine learning models. There are 2 options for building a machine learning model:

What is Azure Machine Learning studio?

A web portal in Azure Machine Learning that contains low-code and no-code options for project authoring and asset management

Some components in the studio

Notebooks:

Write and run your code in Jupyter Notebook servers that are directly integrated in the studio

Azure Machine Learning designer:

Provide a graphical interface. You can drag and drop datasets and modules to create machine learning pipelines

Automated machine learning UI:

Help you choose the best model by iterating over many combinations of algorithms and hyper-parameters, based on a success metric

Data labeling:

Used for creating, managing, monitoring labeling projects such as image classification (includes multi-label and multi-class), object identification with bounded boxes

Data stores:

A reference to the data source location along with a copy of its metadata

Datasets:

Used by Data stores to securely connect to Azure storage services.

Environments:

Encapsulate the environment where training or scoring of your machine learning model happens. It specifies the Python packages, environment variables and software settings.

Runs:

A run is a single execution of a training script. The following information will be stored:

Experiments:

A experiment is a grouping of many runs from a specified script

Models:

Include machine learning models. A model is a piece of code that takes an input and produces output

Compute resources:

A machine or set of machines you use to run your training script or host your service deployment. You can create and configure your own compute resources.

Pipelines:

Help you to create and manage workflows that stitch together machine learning phases. A pipeline might include data preparation, model training, model deployment and inference/scoring phases

Day 2

Day 2

Work with Azure Machine Learning with Python SDK

In this tutorial, I am going to show you how to use Python SDK so that you can commit your code on local computer to Azure workspace and run it with cloud computing resources

Install the Azure Machine Learning SDK

You can use pip to install required package for Azure Machine Learning SDK

Directory structure

We have a picture that illustrates the directory structure of our very first project

Create an Azure Machine Learning Workspace

We create a workspace in the create-workspace.py file:

After running this code, a file named config.json will be created in the .azureml subdirectory, and a workspace will be created:

Create an Azure Machine Learning compute cluster

We will create a compute cluster with 4 nodes. Depending on computational complexity, the number of nodes that are used for running our code will auto scale between zero and four

Define A Convolutional Neural Network

In this project, we will use pytorch framework to build our model We define the architecture of the neural network, and also the forward propagation operation in the model.py file. Our network includes 2 convolutional layers, each followed by a max pooling layer. After that, we have a flatten out layer, then 3 consecutive fully connected layers.

We have the code for training our network in the train.py file. The converge condition for our program is after 2 epochs