The Artificial Intelligence and Machine Learning industry has become one of the most prominent industries with many AI products like virtual assistants, weather forecasts, and many more. AI and ML are becoming our day-to-day necessity with the speed at which it is growing.
But with ML comes hardware requirements, coding skills, and the cost associated with them.
To suffice all ML requirements it is essential to throw light on Azure ML, which is a go-to solution for all ML needs. Azure provides machine learning as a pay-as-you-go service. Azure ML is an end-to-end cloud-based framework that can help you build, manage, and deploy thousands of models, while also providing cloud resources to manage and process all the data that comes along with it.
Features of Azure ML1
- Azure ML Designer – Azure ML designers helps you visually connect datasets and modules on an interactive canvas to create machine learning models. Drag and drop the required modules and connect them accordingly.
- Jupyter Notebooks – Azure ML provides Jupyter Notebook, an interactive web tool that can contain live code as well as expansions in the form of text or multimedia. You can use Jupyter Notebooks provided by Azure or create your own, using Python SDK.
- R scripts – You can write R code or use provided R modules in the ML Designer.
- Many Models Solution Accelerator – It allows you to train and operate up to thousands of machine learning models.
- Visual Studio code extension – These extensions let you work with machine learning models natively in your Visual Studio IDE environment.
- Machine learning CLI – It lets you work with machine learning models using the command line or automate models with scripts.
- Support for open-source framework – You can easily integrate with TensorFlow, PyTorch, and Scikit-Learn.
- Reinforcement learning – support for Ray RLlib.
- Machine learning workflows – you can use MLflow to track deployment patterns and values or use Kubeflow to build a complete workflow.
Azure ML Workspace
What is Azure ML Workspace?
A machine learning workspace is a central, shareable platform that can help in managing resources like compute instances, and assets that were created while training models.
Azure ML offers two main ways to build and operate machine learning algorithms:
- Integrating the SDK and writing code in Python or R
- Using a no-code drag-and-drop designer
Components of Azure ML Workspace2
Below is a diagram that shows the components of Azure ML Workspace.
- Experiments – An Azure ML experiment represents the collection of trials used to validate a user’s hypothesis. The experiment helps you track the following for all the trial runs:
- Snapshot of your scripts
In the case of training the model, you can use this information to determine which training run produced the best model. And once you have a model you like, you can register it with the workspace.
- Registered Models – Register a model using an experiment or add it from the UI. The model will have a name and a version. Azure ML lets you specify extra tags and parameters for each model. You can use the registered model and a scoring script to create a deployment endpoint.
- Compute Targets – It is an environment where you can run your experiments. Azure ML provides compute instances and compute clusters which are cloud resources configured with the Python environment necessary to run Azure ML. These can be scaled up or down depending on the requirements.
- User Roles – You can configure the user roles which enables you to share your workspace with other users, teams, or projects.
- Associated resources – When you create the workspace, associated resources are also created for you, which include –
- Azure storage account – primary data storage location for the workspace, which includes blobs, files, queues, tables, and disks.
- Azure container registry(ACR) – stores Docker container images used during model training and deployment.
- Azure application insights – enables monitoring and analysis of model execution and performance.
- Azure key vaults – stores sensitive data required by the workspace, including secrets.
- Pipelines – An Azure ML pipeline is an independently executable workflow of a complete machine learning task. Subtasks are encapsulated as a series of steps within the pipeline. A pipeline runs as a part of an experiment.
- Deployment Endpoints – A registered model can be deployed to production as a service endpoint. For deploying endpoints you will need the following:
- Environment – includes all dependencies needed to run the model
- Scoring code – a script that accepts inputs, scores them using the model, and then returns model results.
- Inference configuration – references the environment and scoring code, and other components required for running the model as a service
- Datasets – They aid in the management of the data you use for model training and pipeline creation. Azure ML datasets let you access and interact with your data. A dataset is a virtual entity that contains a reference to a data source, as well as a copy of the source’s metadata. The data stays in place so that there is no additional storage cost and no risk to data integrity. Credentials required to access the data, such as Azure subscription and token authorization, are stored separately in the workspace Key Vault.
Azure Workspace Architecture3 –
Reasons to use Azure ML4
Here are a few reasons why you should choose Azure ML for your Machine Learning solutions:
- Machine Learning as a Service
Microsoft Azure offers Azure ML as a pay-as-you-go service. You don’t have to buy complicated hardware or software, you just need to purchase the relevant services and start developing your Machine Learning applications immediately.
- Easy scaling of hardware resources
Azure ML provides an option for multi-node compute clusters that auto-scales each time you submit a run. This saves cost by not wasting extra resources.
- Easy & Flexible building interface
Execute your ML development through the Microsoft Azure ML Studio. It offers drag-and-drop components that minimize the code development and straightforward configuration of properties. Moreover, it helps build, test, and generate advanced analytics based on the data.
- Better Visualization, Logs, and Matrices
Divide the jobs into steps and the Azure ML provides a graphical user interface, where you can easily see how much progress your job has made. Azure ML also provides simple logging and matrices for easy debugging and tracking resource utilization.
- Wide range of supported algorithms
Azure ML supports well-known algorithms which can easily be configured from Azure Designer by a simple drag-and-drop feature. This does not require much prior knowledge of data science. The algorithms can also be used in SDK as well. There is no limit to importing training data and you can pre-process your data easily.
- Easy implementation of web services
Once you are completed with creating the pipeline for the web service, you just need to click the publish button and now you can use the web service from any device by passing valid credentials.
- Great documentation for Machine Learning Solutions
Microsoft Azure offers full stacks of documentation like quick starts, tutorials, references, and lots of examples that can help you to easily build, deploy, manage and access the Machine Learning solutions effectively.
In addition to the above, Azure ML offers the utmost flexibility and extensibility with the feature to include R and Python code. It is beneficial when the built-in Machine learning algorithms and models are not enough. Managing and utilizing big data is always a cumbersome task for enterprises. However, building advanced analytics solutions with Azure ML is more approachable and simple. It provides easier management of everything involved in the ML process. Also as it is a pay-as-you-go service, it is also cost-efficient.