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Azure Machine Learning vs. Vertex AI

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Azure Machine Learning

    Score8.2 out of 10
    N/AMicrosoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.

    $0

    per month

    Vertex AI

    Score8.8 out of 10
    N/AVertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.

    $0

    Starting at

    Pricing
    Azure Machine LearningVertex AI
    Editions & Modules
    Studio Pricing - Free
    $0.00
    per month
    Production Web API - Dev/Test
    $0.00
    per month
    Studio Pricing - Standard
    $9.99
    per ML studio workspace/per month
    Production Web API - Standard S1
    $100.13
    per month
    Production Web API - Standard S2
    $1000.06
    per month
    Production Web API - Standard S3
    $9999.98
    per month
    Imagen model for image generation
    $0.0001
    Starting at
    Text, chat, and code generation
    $0.0001
    per 1,000 characters
    Text data upload, training, deployment, prediction
    $0.05
    per hour
    Video data training and prediction
    $0.462
    per node hour
    Image data training, deployment, and prediction
    $1.375
    per node hour
    Offerings
    Pricing Offerings
    Azure Machine LearningVertex AI
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeOptional
    Additional DetailsPricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
    More Pricing Information
    User Ratings
    Azure Machine LearningVertex AI
    Likelihood to Recommend
    8.0
    (4 ratings)
    6.7
    (10 ratings)
    Likelihood to Renew
    7.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    7.0
    (2 ratings)
    -
    (0 ratings)
    Performance
    -
    (0 ratings)
    7.3
    (7 ratings)
    Support Rating
    7.9
    (2 ratings)
    -
    (0 ratings)
    Implementation Rating
    8.0
    (1 ratings)
    -
    (0 ratings)
    Configurability
    -
    (0 ratings)
    7.0
    (7 ratings)
    User Testimonials
    Azure Machine LearningVertex AI
    Likelihood to Recommend
    Microsoft
    No answers on this topic
    Google
    Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.
    Incentivized
    Read full review
    Pros
    Microsoft
    • User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
    • Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
    • Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
    • Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
    • Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.
    Incentivized
    Read full review
    Google
    No answers on this topic
    Cons
    Microsoft
    No answers on this topic
    Google
    • Customization of AutoML models - A must needed capability to be able to tweak hyperparameters and also working with different models
    • Model Explainability -Providing more comprehensive explanations about how models are utilizing features could be very beneficial
    • Model versioning and experiments tracking - Enhancing the versioning capability could be good for end users
    Incentivized
    Read full review
    Usability
    Microsoft
    No answers on this topic
    Google
    No answers on this topic
    Performance
    Microsoft
    No answers on this topic
    Google
    No answers on this topic
    Support Rating
    Microsoft
    No answers on this topic
    Google
    No answers on this topic
    Implementation Rating
    Microsoft
    No answers on this topic
    Google
    No answers on this topic
    Alternatives Considered
    Microsoft
    No answers on this topic
    Google
    No answers on this topic
    Return on Investment
    Microsoft
    • Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
    • Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
    • Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details
    Incentivized
    Read full review
    Google
    No answers on this topic
    ScreenShots

    Vertex AI Screenshots

    Screenshot of an introduction to generative AI on Vertex AI - Vertex AI Studio offers a Google Cloud console tool for rapidly prototyping and testing generative AI models.Screenshot of gen AI for summarization, classification, and extraction - Text prompts can be created to handle any number of tasks with Vertex AI’s generative AI support. Some of the most common tasks are classification, summarization, and extraction. Vertex AI’s PaLM API for text can be used to design prompts with flexibility in terms of their structure and format.Screenshot of Custom ML training overview and documentation - An overview of the custom training workflow in Vertex AI, the benefits of custom training, and the various training options that are available. This page also details every step involved in the ML training workflow from preparing data to predictions.Screenshot of ML model training and creation -  A guide that shows how Vertex AI’s AutoML is used to create and train custom machine learning models with minimal effort and machine learning expertise.Screenshot of deployment for batch or online predictions - When using a model to solve a real-world problem, the Vertex AI prediction service can be used for batch and online predictions.