Machine Learning
    Why choose Azure Machine Learning?
    • For the flexible interface that minimizes code development
    • Supports a wide-range of well-known algorithms
    • To operationalize at scale
    • Support for the end-to-end machine learning lifecycle

    Azure Machine Learning is an enterprise grade machine learning service to build, test, deploy, and manage high-quality ML models faster on a secure platform. It is designed for individuals and teams that want to deploy MLOps and enable rapid deploy robust ML models. With Azure ML, you can get up to 3x ROI & 70% fewer steps on training models.

    Our team of data scientists at Polestar Solutions build and implement custom solutions to meet your enterprise's unique requirements & accelerate big-data analytics. We develop Machine Learning solutions with Azure ML & Azure Databricks to harness the data efficiently and make the devices & apps smarter for intelligent decision-making.

    Service Capabilities for end-to-end Machine Learning
    Data Preparation

    Useful for data cleaning, labelling, preparation of data with PySpark and create models

    Types of Machine Learning

    Create accurate models rapidly with Automated & Drag-and-drop Machine learning


    Accelerate productivity with Azure Synapse, Azure Cognitive Search, Azure Data Factory, and more


    Create responsible AI dashboards & generate scorecards for better contexualization

    Hybrid & Multi cloud support

    Use simple machine learning to start training or run on Kubernetes clusters or on multi-cloud


    Build all your ML models securely with network isolation, role-based access, and private IP

    Azure Machine Learning Service
    Azure Machine Learning Service
    Azure Machine Learning Services we offer

    Trying to start an ML project can be daunting for enterprises. But with Azure Machine Learning solution, you get end-to-end model pipeline development focused on security, execution, and responsibility. You can build custom AI solutions, get ML based automation, integrate with Azure APIs, and get 24x7 support with us. Some of the key services of Azure ML include:


    With Azure ML you can streamline the deployment of models in multicloud environments. You can automate workflows for Continuous Deployment and Continuous Integration (CI/CD) with pipelines, monitor metrics, and improve governance


    Manage multiple runs for training and experimentation with a secure & comprehensive portfolio. We can help you automatically scale to need your machine learning needs. Wherein you can build ML solutions with custom access, encryption, and improved collaboration & reliability

    Power BI Integration

    Through the combination of Azure Machine Learning and Power BI you can add value to model building by visualize the insights in real-time in Power BI. With this Power BI Integration you can explore the depths of Azure ML's features and breakthrough the chains of traditional reporting

    MLOps Workflow
    MLOps Workflow

    Azure Machine Learning comes as pay-as-you-go advanced and predictive analytics service that doesn't require complex setting up and infrastructure purchases. With the easy & flexible building interface you can drag & drop components which also has readily available well-known algorithms which makes it easier to bring insights to your data.

    Key Features of Azure Machine Learning include compute options for varying machine learning workloads, use Jupyter notebooks/R Studio/Jupyter Labs in conjuction, use datastores to mount data from Azure Storage services, integrate with Github, and create machine learning models through an interactive GUI

    Though there are multiple differences in the platform, the key difference lies in its usage and classification, Azure Databricks can be used as a General analytics tool whereas Azure Machine Learning is an MLaaS tool. The other differences are that though for scalability Databricks is better, Azure ML has better UI & is low-code. Databricks can be used for heavy data preparation and modeling whereas AMLS can be used for advanced analytics and deep learning

    The most well known use case of Azure Machine Learning model is the short replies that are available on Outlook based on the content of the emails, some of the other use cases include sentiment analysis, building recommendation engines, demand forecasting, fraud detection, and more

    Get Started On Your Data Journey with Azure Machine Learning
    Begin Your Azure Cloud Migration Journey Now!
    Why Polestar Solutions for your Azure Machine Learning needs?
    • 7+ Years of Professional Experience
    • Certified Microsoft Azure Experts
    • Faster Deployment
    • Top Cloud Solution Providers
    • 24*7 Customer Support
    • Established architectural planning
    • Low migration risk
    • Integrated microservices