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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Amazon SageMaker enables the identification of various types of unprocessed data, including images, text documents, and videos, while also allowing for the addition of meaningful labels and the generation of synthetic data to develop high-quality training datasets for machine learning applications. The platform provides two distinct options, namely Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which grant users the capability to either leverage a professional workforce to oversee and execute data labeling workflows or independently manage their own labeling processes. For those seeking greater autonomy in crafting and handling their personal data labeling workflows, SageMaker Ground Truth serves as an effective solution. This service simplifies the data labeling process and offers flexibility by enabling the use of human annotators through Amazon Mechanical Turk, external vendors, or even your own in-house team, thereby accommodating various project needs and preferences. Ultimately, SageMaker's comprehensive approach to data annotation helps streamline the development of machine learning models, making it an invaluable tool for data scientists and organizations alike.

Description

Revolutionary machine teaching is here with an exceptionally efficient annotation tool driven by active learning. Prodigy serves as a customizable annotation platform so effective that data scientists can handle the annotation process themselves, paving the way for rapid iteration. The advancements in today's transfer learning technologies allow for the training of high-quality models using minimal examples. By utilizing Prodigy, you can fully leverage contemporary machine learning techniques, embracing a more flexible method for data gathering. This will enable you to accelerate your workflow, gain greater autonomy, and deliver significantly more successful projects. Prodigy merges cutting-edge insights from the realms of machine learning and user experience design. Its ongoing active learning framework ensures that you only need to annotate those examples the model is uncertain about. The web application is not only powerful and extensible but also adheres to the latest user experience standards. The brilliance lies in its straightforward design: it encourages you to concentrate on one decision at a time, keeping you actively engaged – akin to a swipe-right approach for data. Additionally, this streamlined process fosters a more enjoyable and effective annotation experience overall.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

ZenML
Amazon SageMaker
Amazon SageMaker Unified Studio

Integrations

ZenML
Amazon SageMaker
Amazon SageMaker Unified Studio

Pricing Details

$0.08 per month
Free Trial
Free Version

Pricing Details

$490 one-time fee
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Amazon Web Services

Founded

2006

Country

United States

Website

aws.amazon.com/es/sagemaker/data-labeling/

Vendor Details

Company Name

Explosion

Founded

2016

Country

Germany

Website

prodi.gy/

Product Features

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Product Features

Data Labeling

Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Alternatives