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Compare Amazon Personalize vs BigML

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BigML - New SaaS Software
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Overview

Description

Amazon Personalize is a machine-learning web service that helps to improve site's user experience by automatically generating personalized recommendations based on millions of books, ... Read More

BigML is an advanced machine learning platform that makes it incredibly simple to access powerful algorithms and predictive models, quickly and easily. Our cloud and on-premises service is ... Read More

Entry Level Pricing
  • Not Available
  • Starts from $1,000 , Billed Monthly
Free Trial Availability
  • No free trial
User Ratings

4.7

(44)
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Software Demo

Demo

Amazon Personalize has not given any software demo yet

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Features

Total Features

0 Features

6 Features

Unique Features

No features

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    Pricing

    Pricing Option
        Starting From
        • Not Available
        • $1,000 , Billed Monthly
        Pricing Plans
        • Not Available
        • Free

          • Total tasks and storage: Unlimited

          • Max. dataset size: 16 MB per task

          • Max. parallel tasks: 2

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        • Standard

          • Total tasks and storage: Unlimited

          • Max. dataset size: 64 MB per task

          • Max. parallel tasks: 2

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        • BigML Lite NEW

          • 5 users

          • 1 organization

          • BigML standard theme

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        • Browse all pricing plans

        Other Details

        Organization Types supported
        • Small Business
        • Freelancers
        • Large Enterprises
        • Medium Business
        • Individuals
        • Small Business
        • Freelancers
        • Large Enterprises
        • Medium Business
        • Individuals
        Platforms Supported
        • Installed - Windows
        • Browser Based (Cloud)
        • Mobile - iOS
        • Mobile - Android
        • Mobile - Windows
        • Installed - Mac
        • Mobile - BlackBerry
        • Browser Based (Cloud)
        • Installed - Windows
        • Browser Based (Cloud)
        • Mobile - iOS
        • Mobile - Android
        • Mobile - Windows
        • Installed - Mac
        • Mobile - BlackBerry
        • Browser Based (Cloud)
        Modes of support
        • 24/7 (Live rep)
        • Business Hours
        • Online
        • 24/7 (Live rep)
        • Business Hours
        • Online
        API Support
        • Available
        • Available
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        User Reviews

        User Ratings

        No reviews available for the product

        Rating Distribution

        No reviews available for the product

        Excellent

        30

        Very Good

        0

        Good

        0

        Poor

        0

        Terible

        0

        Positive Reviews

        No reviews available for the product

        No reviews available for the product

        Media and Screenshots

        Screenshots

        No screenshots available.

        Dashboard

        3 Screenshots

        Videos

        No videos available.

        video-0

        3 Videos

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        Frequently Asked Questions (FAQs)

        Stuck on something? We're here to help with all the questions and answers in one place.

        Amazon Personalize offers Free Trial, but BigML does not.

        Amazon Personalize is designed for Large Enterprises, Medium Business and Small Business.

        BigML is designed for Large Enterprises, Medium Business and Small Business.

        The starting price of BigML begins at $1,000 when Billed Monthly, while pricing details for Amazon Personalize are unavailable.

        Some top alternatives to Amazon Personalize includes Amazon Comprehend, Acoustic Personalization, MorphL AI, Amazon Lex, Amazon Forecast, Recom.ai, Google Cloud AutoML, BigML, Amazon SageMaker and Azure Machine Learning Studio.

        Some top alternatives to BigML includes SAS Advanced Analytics, Qualified, Workvivo, Backprop, Neuro, BasicAI, Google Cloud AutoML, Azure Machine Learning Studio, Inferrd, ScoopML and Machine Box.

        Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].