Azure Machine Learning
What is Azure Machine Learning?
Azure Machine Learning is a service that allows us to create, manage, test, and monitor Machine learning models in a cloud-based environment. It also supports some of the open-source technologies like PyTorch, TensorFlow, Matplotlib, and Scikit-Learn. The best part of AZ-ML is to train the model and deploy it on the cloud. This service assists us in developing efficient and accurate models. Microsoft Azure offers cloud computing services like Hyperparameter tuning services, Azure Machine Learning Compute, and Azure Databricks.
Azure Machine Learning makes development easy and saves costs. It is possible to build Machine Learning models with drag and drop methods by using Azure Machine Learning Studio. AzueML connects the users with sources like Azure SQL, Hive Query, and other on-premise data sources.
Benefits of Azure Machine Learning:
Below are some of the benefits of Azure Machine Learning:
- Azure Machine Learning provides predictive analysis at a low cost.
- We can use this model in IoT devices, Web Services, and Power BI.
- AZ-ML Studio provides drag and drop workshops without any coding.
- It also provides hyperparameter tuning and independent frameworks.
- In AZ-ML, there is no need for data replication in computing environments. Users can download the data easily once the data source is created.
- The libraries of traditional Machine Learning do not support massive datasets processing, but whereas Azure ML present datasets in the cloud environment.
- Machine learning integration in the cloud plays a vital role in the finance sector and healthcare.
- By using Azure Machine service, the data applications can predict the accurate data outcomes from the machines.
Azure Machine Learning Studio (Classic):
Azure Machine Learning Studio is a collaborative tool with the drag-and-drop environment to test, build, and deploy the solutions of predictive data analytics. AzureML publishes the models in the form of web services consumed by Power BI tools and custom applications. AZ-ML is the point where predictive analytics, data science, and cloud resource meet.
Here is the comparison between Machine Learning Studio (Classic) and Azure Machine Learning:
| Parameter | Machine Learning Studio (Classic) | Azure Machine Learning |
|---|---|---|
| Drag and drop interface | Yes | Yes |
| Modules for Drag and drop interface | Many modules are available | Consists initial set of modules |
| Interfacing Compute Targets | It has a proprietary web service format | AML compute and Azure Kubernetes Service |
| MLOps | Basic deployment and model management | Pipeline versioning, and configurable deployments |
| Hyperparameter tuning and automated model training | No | No |
| Experiment | Scalable | Scalable with computing |
| Training compute targets | CPU support and Proprietary compute target | Supports AZ-ML compute and Notebook VMs. |
| Machine Learning Azure Pipeline | Not supported | Supported |
| Model format | Studio (classic) only | Training job and standard format |
| Coding | No coding is required | Requires coding |
| Algorithms | It has in-built data transformation tools and algorithms | It has independent Machine Learning algorithms |
| Pre-defined algorithms | Here, pre-defined algorithms provide solutions | Here, pre-defined algorithms don’t provide solutions |
Characteristics of Azure Machine Learning Studio (Classic):
- Azure Machine Learning Studio has one module and one dataset.
- The modules can be connected to other modules or the datasets.
- Datasets can be connected only to the modules.
- Each Module must have all the required parameters.
- Every input module ports must have a connection for the data flow.
Datasets:
Dataset is referred to as data that is uploaded to Machine Learning Studio (classic) in the modeling process. Sample datasets include Machine learning Studio for every new experiment.
Examples of Datasets:
- Breast Cancer data
- MPG data for Automobiles
- Forest fires data
Modules:
An algorithm that is performed on the data is called Module. Microsoft Azure Machine Learning Studio has many modules to score, train, and process validation. To configure the internal algorithms of the Module, it should have a set of parameters for the configuration.
Examples of Modules:
- Compute Elementary Statistics
- Conversion to ARFF
- Score model
- Linear Regression
Compute Target:
Compute target is a resource that is associated with hosting deployment services and executing training programs. It is related to the workspace. Users can develop ML compute instance through the Azure Machine Learning SDK and Azure Portal. MS Azure Machine Learning also cannot manage some compute targets; those are referred to as unmanaged compute targets. Those targets are attached outside of the Azure Machine Learning. It requires many steps in enhancing the Machine Learning workloads.
Working of Azure Machine Learning Studio:
Azure ML studio has a drag-and-drop feature, and it does not need coding. There are many sample datasets and pre-defined algorithms in Microsoft Azure ML studio. Regression is used to predict the value. To choose the right algorithm, we have to predict the value. We can also forecast the values by estimating the relationships of the variables. Examples are sales figures prediction, product demand estimation, and so on. The algorithm options contain:
- Poisson Regression
- Ordinal Regression
- Linear Regression
- Fast Forest Quantile Regression
- Bayesian Regression
- Decision Forest Regression
- Neural Network Regression
- Boosted decision tree Regression
They are two options in predicting the data points:
- PCA-based anomaly detection (Abnormal equipment readings, Fraud detection)
- One class SVM
K-means clustering is one of the algorithms for customer segmentation and taste prediction.
The algorithms in predicting two categories are:
- Averaged perceptron
- SVM
- Bayes point machine
- Decision forest
- Boosted decision tree
- Logistic regression
- Decision jungle
- Neural network
The algorithms in predicting multiple categories are:
- Multiclass neural network
- Multiclass decision jungle
- Multiclass logistic regression
- Multiclass decision forest
- One-vs-all multiclass
Azure Machine Learning Deployment Workflow:
The Machine Learning workflow is an iterative data science framework in delivering intelligent applications and predictive analytics solutions. It helps to develop team collaboration. It contains the best distillation practices and structures for data science successful implementation. The goal of Machine Learning is to help the companies fully in analyzing the program. The life cycle outlines execute in the following parameters:
- Data Understanding and Data Acquisition
- Business Understanding
- Deployment
- Modeling
- Customer Consumption and Acceptance
Figure: Machine Learning Workflow
The workspace in MS Azure Machine Learning service represents the collaboration of teams, managing the compute targets, web-service deployments, and docker-images creation. Python is used for the quick start with Microsoft Azure Machine Learning. In AZ-ML, we can create workspaces shared by multiple people. The workspace can be shared among many people by assigning the following roles:
- Contributor
- Owner
- Reader
There are many Azure resources that can be created in implementing a new workspace.
Azure Storage: It is used as a workspace default data storage.
Azure Key Vault: Stores secrets by using sensitive information and compute targets.
Azure Container Registry: It is used to register the Docker containers while deploying a model.
Azure Application Insights: It stores information about models.
Data Scientists build a satisfactory model into production with Azure Machine Learning services.
Steps in Azure Machine Learning Deployment:
- Register the model in the workspace of Azure Machine Learning
- Register the image in a portable container with a scoring script.
- Deploy images to the edge devices as a cloud web-service.
Azure Machine Learning Pipelines:
Azure Machine Learning pipelines are used to perform logical workflow with sequence steps. Each step creates an action in discrete processing. In the initial stage of ML projects, Python script and Jupyter notebook did all the works of resource configuration, run configuration, data preparation, data validation, and Azure workspace. But later ML workflow became a monolithic script for imperative code. By modularizing Machine Learning tasks, pipeline supports imperatives in Computer Science in doing only one thing. Modularity became inevitable for project success in separating the tasks. Tasks include Data access, Workspace configuration, model definition, data preparation, deployment, and configuration.
Procedure to run a Pipeline:
- For every step, the service requires the following
- Operating System (OS) resources
- Hardware compute resources
- Data Inputs
- Software resources
- The service is used to determine the steps and their dependencies in the execution of a dynamic graph.
- While the execution graph is running
- The service can configure the necessary software and hardware environments.
- The step runs in monitoring the information to the experiment object.
- The outputs prepare input steps for the storage.
- Resources are detached and finalized.
Advantages of using Pipelines:
Heterogeneous compute: Use several pipelines in coordinating scalable, storage locations, and Heterogenous compute. Make efficient use by running the compute targets such as GPU Data Science VMs, HDInsight, and Databricks.
Tracking and Versioning: The pipelines use SDK instead of using the manual tracking of data. Users can manage the data and scripts separately for production growth.
Collaboration: By using the pipelines, the Data Scientists collaborate all areas of Machine Learning designs to work on the pipeline steps.
Unattended runs: Pipelines are used to schedule the steps in sequence in an unattended run. Data modeling, Pipelines, and data preparation allow the user to focus on the tasks.
Reusability: Pipeline creates specific scenario templates for batch-scoring and retraining through simple REST calls.
Modularity: Pipelines separate the areas of isolation and concern changes to allow very fast at a higher quality.
Conclusion:
The above article gave an idea about creating, deploying, and testing an Azure Machine Learning model. We have provided detailed information about Azure Machine Learning workflow and Pipelines. Now, it is easy for you to leverage the Machine Learning libraries with Microsoft Azure. Still, you have any queries please feel free to ask in the comment section.
