Best AWS Marketplace Software - Page 76

How Many AWS Marketplace Software Products Does G2 Track?

Total Products under this Category: 2,493

Category Stats (Sep 2026)

  • Average Rating: 4.39/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Parallel Task API (+2.42%) - Among all products in this category, Parallel Task API recorded the largest rating increase compared to last month

Last updated: September 15, 2026

How Does G2 Rank AWS Marketplace Software Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 30,200+ Authentic Reviews
  • 2,493+ Products
  • Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

Education Video CDN System

Education Edition: Subscription for peer-to-peer education video distribution for schools, colleges, and learning institutes. Includes the server and client end solution with unlimited videos and bandwidth. Proprietary streaming algorithms founded on industry standards such as WebRTC. Customers have the option to brand the player using standardized Javascript and CSS templates. Deployment currently supported on EC2, Containers, Lambda+S3, or Custom AMI

Average Rating: 5.0/5.0

Total Reviews: 1

How Do G2 Users Rate Education Video CDN System?

  • Quality of Support: 6.7/10 (Category avg: 8.5/10)
  • Ease of Use: 8.3/10 (Category avg: 8.7/10)

Who Is the Company Behind Education Video CDN System?

Who Uses This Product?

  • Company Size: 100% Medium

What Are Recent G2 Reviews of Education Video CDN System?

Efficient and clean text operations

The "Efficient and Clean Text Operations" solution is a comprehensive, cloud-based service designed to streamline the processing of unstructured text data. By leveraging advanced machine learning technologies, it automates the extraction, analysis, and management of text from various document formats, including PDFs, images, and scanned documents. This solution is particularly beneficial for organizations dealing with large volumes of textual data, enabling them to derive actionable insights efficiently and accurately. Key Features and Functionality: - Automated Text Extraction: Utilizes machine learning to extract text, handwriting, and data from scanned documents, recognizing complex elements like tables and forms. - Natural Language Processing : Employs NLP to analyze text, identifying entities, key phrases, sentiments, and other elements to develop insights about document content. - Data Preparation and Cleaning: Offers tools for data validation, normalization, and transformation, ensuring high-quality data for analysis. - Scalable Architecture: Designed to handle large datasets efficiently, supporting both real-time and batch processing to meet diverse operational needs. Primary Value and Problem Solved: This solution addresses the challenges associated with managing and analyzing vast amounts of unstructured text data. By automating the extraction and processing of text, it significantly reduces manual effort, minimizes errors, and accelerates data-driven decision-making. Organizations can enhance operational efficiency, improve data accuracy, and unlock valuable insights from their textual data assets.

Who Is the Company Behind Efficient and clean text operations?

EfficientNet

This model provides top-k category predictions out of 1000 classes on ImageNet. This network provides state of the art accuracy on ImageNet validation (top-1: 82.242 - top-5: 96.114) at the same time it provides a smaller model and faster inference. It features a simple pricing model where only pay for what you use with a simple metered pricing model.

Average Rating: 4.5/5.0

Total Reviews: 1

How Do G2 Users Rate EfficientNet?

  • Quality of Support: 8.3/10 (Category avg: 8.5/10)
  • Ease of Use: 10.0/10 (Category avg: 8.7/10)

Who Is the Company Behind EfficientNet?

Who Uses This Product?

  • Company Size: 100% Small

What Are Recent G2 Reviews of EfficientNet?

EfficientNet B0

This is a Image Classification model from TensorFlow Hub

Who Is the Company Behind EfficientNet B0?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

EfficientNet B1 Lite

This is a Image Classification model from TensorFlow Hub

Who Is the Company Behind EfficientNet B1 Lite?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

EfficientNet B2 Lite

This is a Image Classification model from TensorFlow Hub

Who Is the Company Behind EfficientNet B2 Lite?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

EfficientNet B4 Lite

This is a Image Classification model from TensorFlow Hub

Who Is the Company Behind EfficientNet B4 Lite?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

EfficientNet B6

This is a Image Classification model from TensorFlow Hub

Who Is the Company Behind EfficientNet B6?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

EfficientNet B7

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Who Is the Company Behind EfficientNet B7?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN

EfficientNet - Train Image Classifier

This Algorithm allows you to easily train a model for image classification based on the state of the art EfficientNet algorithm. After training a custom model you can deploy the trained model as an endpoint. It features a simple pricing model where only pay for what you use with a simple metered pricing model

Who Is the Company Behind EfficientNet - Train Image Classifier?

Efiia Governance, Risk, Compliance (GRC) Support

The Efiia GRC Support provides US Federal government Information System Owners (ISOs) subject matter expertise in Governance, Risk and Compliance for cloud computing programs in AWS GovCloud. The Efiia GRC ensures that ISOs adheres to common governance, compliance processes, conducts audits, and ensures that technologies and business operations are structured and configured for data protection and compliance.

Who Is the Company Behind Efiia Governance, Risk, Compliance (GRC) Support?

  • Seller: Efiia
  • Year Founded: 2001
  • HQ Location: Washington, US
  • Twitter: @efiia
    4 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    21 employees on LinkedIn®

Efiia ISSO Support

The Efiia ISSO Support team delivers a comprehensive suite of Security Assessment and Authorization (SA&A) services for US Federal government Information System Owners (ISOs) subject matter expertise in cloud computing programs in AWS GovCloud. The Efiia ISSO Support team ensures that ISOs are delivering services in alignment with contractually agreed terms and conditions including security requirements, privacy requirements, and service level agreements.

Who Is the Company Behind Efiia ISSO Support?

  • Seller: Efiia
  • Year Founded: 2001
  • HQ Location: Washington, US
  • Twitter: @efiia
    4 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    21 employees on LinkedIn®

EKS-Milpa

EKS-Milpa is a comprehensive solution designed to streamline the deployment and management of Kubernetes clusters on Amazon Elastic Kubernetes Service (EKS. It offers a suite of tools and configurations that simplify the setup process, ensuring that users can efficiently run containerized applications without the complexities typically associated with Kubernetes management. Key Features and Functionality: - Automated Cluster Provisioning: EKS-Milpa automates the creation and configuration of EKS clusters, reducing manual intervention and potential errors. - Optimized Node Management: It provides pre-configured Amazon Machine Images (AMIs optimized for EKS, ensuring compatibility and performance. - Security Enhancements: The solution integrates best practices for security, including IAM roles and policies tailored for Kubernetes workloads. - Scalability Support: EKS-Milpa facilitates seamless scaling of applications by managing node groups and resource allocation efficiently. - Monitoring and Logging: It incorporates tools for monitoring cluster health and logging, aiding in proactive maintenance and troubleshooting. Primary Value and Problem Solved: EKS-Milpa addresses the challenges of setting up and managing Kubernetes clusters on AWS by providing an automated, secure, and optimized environment. This allows developers and operations teams to focus on deploying and managing applications rather than dealing with the intricacies of cluster configuration and maintenance. By simplifying these processes, EKS-Milpa accelerates time-to-market for applications and enhances operational efficiency.

Who Is the Company Behind EKS-Milpa?

  • Seller: Elotl
  • Year Founded: 2016
  • HQ Location: San Francisco, US
  • LinkedIn® Page: www.linkedin.com
    12 employees on LinkedIn®

ELECTRA-Base++

It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is pre-trained on WikiPedia and BookCorpus returns an embedding of the input text. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Who Is the Company Behind ELECTRA-Base++?

  • Seller: Amazon Web Services (AWS)
  • Year Founded: 2006
  • HQ Location: Seattle, WA
  • Twitter: @awscloud
    2,232,483 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    147,094 employees on LinkedIn®
  • Ownership: NASDAQ: AMZN
Neeraja Prakash
NP
Researched and written by Neeraja Prakash
Updated June 16, 2025