Name: Azure Data Science Virtual Machines (DSVM)
Rating: 8.4 (3 reviews)
Author: Microsoft

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

Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization.  Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.

Standard

Premium

IBM® ILOG® CPLEX® Optimization Studio is a prescriptive analytics solution that enables rapid development and deployment of decision optimization models using mathematical and constraint programming.

Azure Data Science Virtual Machines (DSVM)

Data Science

Likelihood to Recommend

Platform Connectivity

Data Exploration

Data Preparation

Platform Data Modeling

Model Deployment

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

MDM Integration

Visualization

Interactive Data Analysis

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Multiple Model Development Languages and Tools

Automated Machine Learning

Single platform for multiple model development

Self-Service Model Delivery

Flexible Model Publishing Options

Security, Governance, and Cost Controls

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Ability to connect to a wide variety of data sources

Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

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

Automatic detection of data formats and schemas

Integration with MDM and metadata dictionaries

Ability to explore data and develop insights

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

Ability to analyze data interactively using Python or R Notebooks

Access to visual processors for data wrangling

Use visual tools for standard transformations

Library of processors for data quality checks

Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.

Tools to help automate algorithm development

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

Multiple model delivery modes to comply with existing workflows

Tools for deploying models into production

Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.

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

Senior Devops Engineer

Azure Data Science Virtual Machines (DSVM) 2022-01-12 01:06:32

HashiCorp Terraform

DevOps Engineer

Azure Data Science Virtual Machines (DSVM) 2021-12-08 13:07:24

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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.

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

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.

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

Leveraging data.
Computer vision.
Data science.

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

Azure Data Science Virtual Machines (DSVM)

Overview

What is Azure Data Science Virtual Machines (DSVM)?