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AWS SageMaker Ground Truth VS Labelbox

Compare AWS SageMaker Ground Truth VS Labelbox and see what are their differences

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AWS SageMaker Ground Truth logo AWS SageMaker Ground Truth

Build highly accurate training datasets using machine learning and reduce data labeling costs by up to 70%.

Labelbox logo Labelbox

Build computer vision products for the real world
  • AWS SageMaker Ground Truth Landing page
    Landing page //
    2023-04-14
  • Labelbox Landing page
    Landing page //
    2023-08-20

A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.

AWS SageMaker Ground Truth features and specs

  • Scalability
    AWS SageMaker Ground Truth can easily handle large datasets, making it suitable for organizations that require scalable labeling solutions.
  • Integration
    Ground Truth is integrated with AWS services, allowing easy access to machine learning models and seamless workflow within the AWS ecosystem.
  • Automated Labeling
    It offers automated data labeling using machine learning, which can reduce the time and costs associated with manual labeling.
  • Cost-Effectiveness
    The pay-as-you-go pricing model can be cost-effective, particularly when utilizing automated labeling to reduce the need for manual intervention.
  • Quality Management
    Ground Truth includes tools for managing labeling quality, like dynamic custom workflows and an audit trail to ensure high-quality outcomes.

Possible disadvantages of AWS SageMaker Ground Truth

  • Complexity
    Setting up and configuring Ground Truth may require a steep learning curve and expertise in AWS services, which can be challenging for new users.
  • Cost for Manual Labeling
    While automated labeling is cost-effective, projects that rely heavily on manual labeling can incur significant expenses, especially with large-scale data.
  • Limited Non-Technical User Accessibility
    The service may not be as user-friendly for those who lack technical expertise or familiarity with AWS, potentially limiting its accessibility to non-technical users.
  • Dependency on AWS Ecosystem
    Ground Truth is tightly integrated into the AWS ecosystem, which can be limiting for organizations that use a multi-cloud strategy or non-AWS resources.
  • Data Privacy Concerns
    Using a cloud-based service for data labeling can raise data privacy and security concerns, particularly for sensitive or regulated datasets.

Labelbox features and specs

  • User-Friendly Interface
    Labelbox features a clean, intuitive interface that makes it easy for users to navigate and manage their projects, even for those who are new to data labeling.
  • Collaboration Tools
    The platform includes robust collaboration tools, allowing multiple team members to work together efficiently on the same project and oversee progress in real-time.
  • API Integration
    Labelbox provides a powerful API that enables seamless integration with other tools and systems, which can help automate workflows and enhance productivity.
  • Comprehensive Annotations
    The platform supports a wide range of annotation types including bounding boxes, polygons, and more. This flexibility allows users to create detailed and precise annotations for diverse use cases.
  • Scalability
    Labelbox is designed to scale with your needs, making it suitable for small projects as well as large enterprises requiring high-volume data labeling.
  • Quality Assurance Features
    Labelbox includes features for quality control and assurance, such as review workflows and consensus scoring, to ensure the accuracy and reliability of labeled data.
  • Data Security
    With strong security protocols in place, Labelbox ensures that sensitive data is protected, meeting compliance standards for various industries.

Possible disadvantages of Labelbox

  • Cost
    Labelbox can be expensive, especially for small teams or startups. The cost might be prohibitive for those with limited budgets.
  • Learning Curve
    Despite its user-friendly interface, some advanced features have a learning curve, requiring time and training to leverage the platform's full potential.
  • Dependency on Internet Connection
    Since Labelbox is a cloud-based platform, a stable internet connection is required. Any internet issues can disrupt workflow and access.
  • Limited Offline Capabilities
    The platform's reliance on being cloud-based means it offers limited offline capabilities, restricting users who might need to work without internet access.
  • Feature Limitations on Basic Plans
    Some advanced features and integrations are only available in higher-tier plans, which can be restrictive for users on basic subscription plans.
  • Integration Complexity
    While powerful, API integrations can be complex and may require technical expertise to set up and maintain effectively.

Analysis of Labelbox

Overall verdict

  • Labelbox is considered a good tool for data labeling, particularly in the context of machine learning and artificial intelligence projects.

Why this product is good

  • User-Friendly Interface: Labelbox offers an intuitive interface that facilitates easy navigation and efficient labeling, making it accessible for both experienced and new users.
  • Customization: It provides customizable workflows that can adapt to specific project needs, enhancing productivity and flexibility.
  • Collaboration Features: The platform supports collaboration among team members, allowing for seamless communication and efficient coordination.
  • Scalability: Labelbox is designed to handle large datasets, making it suitable for projects of varying sizes, including enterprise-level operations.
  • Integration Capabilities: The tool integrates well with other data management and machine learning frameworks, allowing for streamlined workflows.

Recommended for

  • Organizations involved in machine learning and AI development, especially those focusing on image and video data.
  • Data science teams needing a robust labeling tool that can handle large volumes of data efficiently.
  • Companies seeking a scalable solution for collaborative data annotation projects.
  • Developers and researchers who require customizable workflows and integrations with other ML tools.

AWS SageMaker Ground Truth videos

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Labelbox videos

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Review App : Labelbox

More videos:

  • Review - Machine Learning Support Engineer at Labelbox
  • Review - Bounding box annotation with Labelbox

Category Popularity

0-100% (relative to AWS SageMaker Ground Truth and Labelbox)

AWS SageMaker Ground Truth

Labelbox

Data Science And Machine Learning
32 32%
Data Science And Machine Learning
68% 68
Data Labeling
17 17%
Data Labeling
83% 83
Image Annotation
18 18%
Image Annotation
82% 82
AI
28 28%
AI
72% 72

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare AWS SageMaker Ground Truth and Labelbox

AWS SageMaker Ground Truth Reviews

We have no reviews of AWS SageMaker Ground Truth yet.
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Labelbox Reviews

  1. Sharon
    · manager at Mcormicki ·
    Unreliable

    Service goes down often. Very slow team. Slow support.

    🏁 Competitors: Diffgram
    👎 Cons:    Slow|Bad support
Top Video Annotation Tools Compared 2022
However, Labelbox only accepts .mp4 files into their platform, and only their most basic annotation modes have the full scope of video annotation options. When annotating videos with segmentation masks, annotators must step through each frame to view their work – there is no playback option.
Source: innotescus.io

Social recommendations and mentions

Based on our record, Labelbox should be more popular than AWS SageMaker Ground Truth. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

AWS SageMaker Ground Truth mentions (3)

  • [D] What are people using to organize large groups of people for data labelling?
    Perhaps https://aws.amazon.com/sagemaker/data-labeling/ ? Source: almost 3 years ago
  • Top 5 AWS ML Sessions to Attend at AWS re:Invent 2021
    In this session you will discover how to use Amazon SageMaker to prepare data for machine learning in minutes. SageMaker provides data preparation tools that make it easier to label, prepare, and analyse your data. Walk through a complete data-preparation workflow, including how to use SageMaker Ground Truth to label training datasets, as well as how to extract data from numerous data sources, convert it using... - Source: dev.to / over 3 years ago
  • Blocked by MLData…it was only a matter of time
    As for who run MLD I guess It’s Amazon itself, have a look at this https://aws.amazon.com/sagemaker/groundtruth/. I speculate that multiple companies use this resource and they are the one responsible to upload the correct instructions, Amazon just redirect the labeling job for us using and requester account in mTurk, that explains why the communication is unacceptable with this requester. Source: over 3 years ago

Labelbox mentions (8)

  • Ask HN: Who is hiring? (October 2022)
    Labelbox | Remote | Frontend / WebGL, Backend, Engineering Managers | https://labelbox.com Labelbox is building the training data platform to power breakthroughs in machine learning. We provide an end to end solutions for the full AI lifecycle from creating catalogs of unstructured data all the way to building the tools for humans to label the data to teach machines. Why choose us? - Source: Hacker News / over 2 years ago
  • Model Assisted Labeling using Label box
    Hey, I have currently developed a U-Net model for segmentation and I am trying to use the model assisted labeling feature on LabelBox to annotate some masks, so I can save time on relabeling. I am just wondering if anyone is familiar with this feature or can give me a step by step guideline on how to go about doing this. I went through the examples on their GitHub but I’m honestly still very confused. Any help... Source: almost 3 years ago
  • What MDR is doing: a Machine Learning perspective
    By now, I hope you see where I'm going with this. What is MDR doing? They're creating the labelled data used to train severance chips. They get a raw download of human brains in encoded format, and go about manually labelling the different pieces based on their most basic elements. Then, based on this manually labelled data, an algorithm can be trained to create a severance chip. MDR is basically Labelbox for... Source: about 3 years ago
  • [D] Any recommendations for image annotation software .
    LabelBox - they provide free versions for research. Source: about 3 years ago
  • Video box annotation tool for Google Cloud Video Intelligence autoML CSV format ?
    Doing some progress, labelbox.com allows me to do the Video annotation, and access all data through python SDK/API... Working on converting myself to CSV GCP format :-). Source: over 3 years ago
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What are some alternatives?

When comparing AWS SageMaker Ground Truth and Labelbox, you can also consider the following products

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...

Computer Vision Annotation Tool (CVAT) - Powerful and efficient Computer Vision Annotation Tool (CVAT) - opencv/cvat

Playment - Playment is a fully-managed solution offering training data for AI, transcription, data collection and enrichment services at scale.

CloudFactory - Human-powered Data Processing for AI and Automation

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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