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Azure Data Science Virtual Machines (DSVM)

Score8.4 out of 10

3 Reviews and Ratings

What is Azure Data Science Virtual Machines (DSVM)?

Available on Microsoft's Azure platform, Data Science Virtual Machines (DSVMs) are comprehensive pre-configured virtual machines for data science modelling, development and deployment.

Categories & Use Cases

Top Performing Features

  • Extend Existing Data Sources

    Use R or Python to create custom connectors for any APIs or databases

    Category average: 8.9

  • Automatic Data Format Detection

    Automatic detection of data formats and schemas

    Category average: 9.2

  • MDM Integration

    Integration with MDM and metadata dictionaries

    Category average: 7.8

Areas for Improvement

  • Visualization

    The product’s support and tooling for analysis and visualization of data.

    Category average: 8.3

  • Single platform for multiple model development

    Single place to build, validate, deliver, and monitor many different models

    Category average: 9.5

  • Security, Governance, and Cost Controls

    Built-in controls to mitigate compliance and audit risk with user activity tracking

    Category average: 8.6

Best suitable solution for ML and AI based applications.

Use Cases and Deployment Scope

I am using the Azure DSVM for [an ]AI-based product on which my company is working. We are looking for a machine [that] can be used to train AI models and have a maximum throughput with highly saleable options. The Azure DSVM provides all the functionalities which we are currently using in the organization.

Pros

  • Machine Learning
  • Power BI
  • Data Warehouse

Cons

  • Azure DSVM pricing must be reduced so that an AI-based start-up can use the Azure DSVM.
  • Azure must create an environment to use Azure DSVM offline as well.
  • Lack of frameworks

Return on Investment

  • Azure DSVM is little costly with long term support for ML based environments.
  • Azure DSVM is very good for short tasking and costs us [a] little low than the on-prem server.
  • [Scaling] option is very convenient.

Alternatives Considered

Amazon SageMaker

Azure DSVM

Use Cases and Deployment Scope

I use them to perform tasks that involve high processing in which the DSVM makes things faster compared to being used with a CPU.

Pros

  • Leveraging data.
  • Computer vision.
  • Data science.

Cons

  • Price is expensive.

Return on Investment

  • It is cheaper than buying a DSVM and having it there until it's obsolete.

Other Software Used

Terraform, by HashiCorp