What Does Automated Machine Learning In Azure Machine Learning Enable You To Do?

We are excited to announce the new automated machine learning (automated ML) features now available with Azure Machine Learning service. Automated ML enables the automation of model selection & hyperparameter tuning, significantly reducing the time required to develop machine learning models from weeks or months to just days. This allows more time to be allocated to addressing business challenges.

The development of automated ML was motivated by our dedication to enhancing the efficiency of data scientists & making AI accessible to all. By simplifying the process of machine learning, automated ML empowers domain experts within organizations to quickly create & implement machine learning solutions.

Comprehensive look at Automated Machine Learning

Automated machine learning, also referred to as Automated ML or AutoML, is the method of creating a machine learning model. It streamlines the time-consuming & repetitive tasks involved in model creation.

Developing a traditional machine learning model typically demands a deep understanding of different machine learning algorithms & requires significant time to construct an effective predictive model. With Azure Automated ML, it is possible to build an effective model without investing extensive time.

What does Azure Machine Learning entail?

azure machine learning

Azure Machine Learning is a cloud-based service designed to speed up & oversee the various stages of a machine learning (ML) project. It is utilized by ML professionals, data scientists, & engineers to facilitate the training & deployment of models, as well as the management of machine learning operations (MLOps).

Whether creating a model within Machine Learning or utilizing one from an open-source platform like PyTorch, TensorFlow, or scikit-learn, MLOps tools are available to aid in monitoring, retraining, & redeploying models.

For whom is Azure Machine Learning intended?

Machine Learning is designed for individuals & teams who are implementing MLOps in their organization to deploy ML models in a secure & traceable production environment. Data scientists & ML engineers can leverage tools to speed up & automate their daily tasks. Developers can utilize tools to seamlessly integrate models into applications or services.

Platform developers can access a comprehensive set of tools supported by robust Azure Resource Manager APIs to build advanced ML tooling. Enterprises operating in the Microsoft Azure cloud can take advantage of familiar security measures & role-based access control for their infrastructure. They can establish a project to restrict access to sensitive data & specific operations.

Enhancing productivity for all members of the team

ML projects typically demand a diverse team with a range of skills for development & upkeep. Machine Learning offers tools to facilitate:

– Team collaboration through shared notebooks, compute resources, serverless compute, data, & environments

– Model development for fairness & explainability, tracking & auditability to meet lineage & audit compliance needs

– Quick & easy deployment of ML models at scale, along with efficient management & governance through MLOps

– Running machine learning workloads anywhere with integrated governance, security, & compliance measures

Platform tools that are compatible across different systems & cater to your requirements

Platform tools that are compatible across different systems to cater to your requirements. Every member of an ML team has the flexibility to use their preferred tools for various tasks such as conducting rapid experiments, hyperparameter-tuning, creating pipelines, or managing inferences. You can utilize familiar interfaces like Azure Machine Learning studio, Python SDK (v2), Azure CLI (v2), & Azure Resource Manager REST APIs.

Throughout the Machine Learning development cycle, you can collaborate with others to refine the model & easily share & access assets, resources, & metrics for your projects through the Machine Learning studio UI.

Studio

The Machine Learning studio provides various authoring experiences based on the project type & your level of ML expertise, all without the need for installation. You can write & execute code in managed Jupyter Notebook servers within the studio.

Additionally, you can analyze & enhance your experiments through visualization of run metrics. The Azure Machine Learning designer allows you to train & deploy ML models without coding by simply dragging & dropping datasets & components to create ML pipelines. You can also utilize the automated machine learning UI for easy creation of automated ML experiments.

Furthermore, the Machine Learning data labeling feature facilitates efficient coordination of image or text labeling projects.

Being prepared for enterprise-level operations & ensuring robust security measures

Machine Learning is seamlessly integrated with the Azure cloud platform to enhance the security of ML projects. This integration includes features such as Azure Virtual Networks with network security groups, Azure Key Vault for securely storing access information for storage accounts, & Azure Container Registry set up behind a virtual network.

Azure integrations for comprehensive solutions

Additional integrations with Azure services facilitate the support of an end-to-end ML project. These integrations encompass:

  • Azure Synapse Analytics, utilized for processing & streaming data with Spark.
  • Azure Arc, enabling the operation of Azure services in a Kubernetes environment.
  • Various storage & database options, including Azure SQL Database & Azure Blob Storage.
  • Azure App Service, providing the capability to deploy & oversee ML-powered applications.
  • Microsoft Purview, offering the ability to explore & categorize data assets throughout your organization.

Workflow for machine learning projects

In general, models are created within a project that has specific objectives & goals. Projects often involve collaboration among multiple individuals. The process of developing models involves iterative experimentation with data, algorithms, & models.

Project lifecycle

The lifecycle of a project may vary, but it typically follows a pattern similar to the diagram shown.

A workspace is used to organize a project & facilitate collaboration among multiple users working towards a shared objective. Users within a workspace can easily share the outcomes of their experimentation in the studio user interface, or utilize versioned assets such as environments & storage references for various tasks.

Once a project is prepared for operational use, the work of users can be automated within an ML pipeline & scheduled or triggered via HTTPS request.

Models can be deployed to a managed inferencing solution for both real-time & batch deployments, eliminating the need for the typical infrastructure management required for model deployment.

Train models

Azure Machine Learning allows you to execute your training script in the cloud or construct a model from the beginning. Many customers bring models they have developed & trained in open-source frameworks to operationalize them in the cloud.

Accessible & compatible

Data scientists can utilize models in Azure Machine Learning that they have developed in popular Python frameworks like:

– PyTorch

– TensorFlow

– scikit-learn

– XGBoost

– LightGBM

Other languages & frameworks are also compatible:

– R

– .NET

Automated feature engineering & algorithm selection

In traditional machine learning, data scientists often spend a significant amount of time & effort selecting the appropriate data features & algorithms for training based on their prior experience & intuition. Automated Machine Learning (AutoML) streamlines this process, offering the option to utilize it either through the Machine Learning studio UI or the Python SDK.

Hyperparameter optimization

Hyperparameter tuning

Hyperparameter tuning, a potentially laborious task, can be automated within Machine Learning for commands with customizable parameters, requiring minimal adjustments to the job definition. The results are conveniently visualized within the studio.

Multinode distributed training

The efficiency of deep learning & , at times, classical machine learning training jobs can be greatly enhanced through multinode distributed training. Azure Machine Learning provides access to the latest GPU options through compute clusters & serverless compute.

Supported via Azure Machine Learning Kubernetes, Azure Machine Learning compute clusters, & serverless compute:

  • PyTorch
  • TensorFlow
  • MPI

Users have the option to utilize MPI distribution for Horovod or custom multinode logic. Additionally, Apache Spark is supported through serverless Spark compute & attached Synapse Spark pool, which utilize Azure Synapse Analytics Spark clusters.

Implement the models

Training models in a highly parallel manner can be necessary when scaling a machine learning project, especially for tasks like demand forecasting across multiple stores. Once a model is ready, it needs to be deployed for production. Azure Machine Learning provides managed endpoints for both batch & real-time model scoring.

Batch scoring processes data in parallel on compute clusters, while real-time scoring provides near real-time responses via HTTPS, with the ability to split traffic across multiple model deployments for testing & gradual adoption of new versions.

MLOps: DevOps principles & practices in the field of machine learning

MLOps, also known as DevOps for machine learning, is the process of developing models for production. It is important for the model’s lifecycle, from training to deployment, to be auditable & potentially reproducible.

Machine Learning is designed with the model lifecycle in mind, allowing for auditing of specific commits & environments. Key features that enable MLOps include git integration, MLflow integration, machine learning pipeline scheduling, Azure Event Grid integration for custom triggers, & ease of use with CI/CD tools like GitHub Actions or Azure DevOps.

In addition, Machine Learning includes features for monitoring & auditing, such as job artifacts (code snapshots, logs, & other outputs) & lineage between jobs & assets (containers, data, & compute resources). Moreover, the airflow-provider-azure-machinelearning package allows Apache Airflow users to submit workflows to Azure Machine Learning.

Read Also: What Does The Term Feature Extraction Refer To In Machine Learning?

Understanding the Functioning of Automated ML in Azure

The process of Automated ML in Azure involves creating multiple pipelines during training to determine the best ML algorithm for the data, as well as handling feature selection & pre-processing. Azure ML pipeline acts like a flowchart for data flow in model building.

The steps to design & run automated ml in the azure workspace include identifying the best algorithm for the problem, choosing deployment method, specifying training data source & format, configuring compute targets, setting Auto ML parameters, submitting the trained model, & reviewing the score. Check out our blog post on DP 100 Exam for an overview.

Conclusion

When developing machine learning solutions, data scientists need to analyze numerous characteristics of machine learning models & carefully consider the pros & cons of each before selecting the most suitable one for a specific business problem. Factors such as speed & automation versus accuracy, simplicity & interpretability versus complexity & precision, & many others need to be taken into account.

Our goal is to offer complete control & transparency into all the models that automated ML produces, allowing you to make informed decisions based on the trade-offs that align with your business problem. The models & pipelines generated by automated ML are standard Python pipelines that can be dissected & fine-tuned according to your preferences.

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