List of Apache MXNet Customers

Customer	Industry	Empl.	Revenue	Country	Vendor	Application	Category	When	SI	Insight Source
Curalate	Communications	140	$14M	United States	Apache Software	Apache MXNet	AI Frameworks and Libraries	2018	n/a	In 2018, Curalate deployed Apache MXNet in the category to train image recognition models for its intelligent product tagging service. The project used MXNet with the Gluon API in a US-based AWS environment and leveraged Amazon SageMaker to move models from experimentation to production model hosting and orchestration. Implementation focused on supervised image model training and inference pipelines, using Apache MXNet for model definition, Gluon for iterative development and prototyping, and SageMaker for managed training jobs and endpoint provisioning. The solution produced inference services that matched social images to product catalog entries, forming the core of Curalate’s social commerce tagging capability. Operational scope centered on brand customers and social commerce workflows, with the tagging service integrated into image ingestion channels and product catalog matching processes. Business functions impacted included social merchandising and marketing automation, where automated image tagging fed downstream catalog enrichment and product linking for campaigns. Governance emphasized a streamlined experimentation to production path, standardizing MXNet model artifacts, training configurations, and SageMaker deployment patterns to reduce handoff friction between data science and engineering teams. The deployment using Apache MXNet improved tagging speed and accuracy for brand customers while consolidating model lifecycle steps into a repeatable AWS and SageMaker based pipeline.
EagleView Technologies	Professional Services	1000	$140M	United States	Apache Software	Apache MXNet	AI Frameworks and Libraries	2018	n/a	In 2018, EagleView Technologies implemented Apache MXNet Category "" to build deep learning models that analyze aerial and satellite imagery for rapid property damage assessment for insurance clients. The deployment focused on automated image classification and per address damage scoring workflows to enable assessments within 24 hours after disasters. Apache MXNet was used to train convolutional neural network models and deploy them into a production inference pipeline, leveraging GPU accelerated training on cloud infrastructure. The implementation included data preprocessing, model training, evaluation, and model level scoring capabilities to produce per address damage assessments. The US based deployment scaled on AWS GPU instances and used Amazon ECS for production inference, instrumenting GPU backed containers for real time batch processing of post event imagery. Operational coverage centered on insurance claims and property assessment teams, with production inference integrated into EagleView operational pipelines for rapid turnaround. Governance emphasized model evaluation and comparability to human adjusters, the project reported model level accuracy comparable to human adjusters, for example about 96% per address accuracy in a published case. The architecture combined Apache MXNet with cloud GPU scaling and containerized inference on ECS to support time sensitive insurance workflows.
Nexity	Construction and Real Estate	3562	$3.9B	France	Apache Software	Apache MXNet	AI Frameworks and Libraries	2020	n/a	In 2020, Nexity used Apache MXNet and the GluonCV toolkit in a France-based internal hackathon to build a pose-detection proof-of-concept that automatically tallied show-of-hand votes during meetings. The implementation leveraged Apache MXNet for model definition and GluonCV for off-the-shelf computer vision operators, framing the project explicitly as a research PoC to evaluate operational CV use cases within the real-estate and facilities context. The PoC implemented a pose-detection workflow that combined model training and inference pipelines, relying on GluonCV model components for key capabilities such as keypoint detection and skeleton estimation. Apache MXNet served as the core machine learning framework for configuring the models and running inference, with limited tuning during the hackathon timeframe, and the team reported roughly a 70% success rate without heavy optimization. Operationally the PoC ran on Amazon SageMaker, using SageMaker to host training and inference experiments at the hackathon scale, and the solution was scoped to internal meeting environments in France. Governance remained at an experimental PoC level with outcomes used to assess applicability, and the exercise demonstrated Apache MXNet applicability for real-estate operational computer-vision use cases without implying production rollout or replacement of any named prior system.

Logo

Customer

Industry

Empl.

Revenue

Country

Vendor

Application

Category

When

Insight

Insight Source

Curalate

Communications

140

$14M

United States

Apache Software

Apache MXNet

AI Frameworks and Libraries

2018

n/a

In 2018, Curalate deployed Apache MXNet in the category to train image recognition models for its intelligent product tagging service. The project used MXNet with the Gluon API in a US-based AWS environment and leveraged Amazon SageMaker to move models from experimentation to production model hosting and orchestration. Implementation focused on supervised image model training and inference pipelines, using Apache MXNet for model definition, Gluon for iterative development and prototyping, and SageMaker for managed training jobs and endpoint provisioning. The solution produced inference services that matched social images to product catalog entries, forming the core of Curalate’s social commerce tagging capability. Operational scope centered on brand customers and social commerce workflows, with the tagging service integrated into image ingestion channels and product catalog matching processes. Business functions impacted included social merchandising and marketing automation, where automated image tagging fed downstream catalog enrichment and product linking for campaigns. Governance emphasized a streamlined experimentation to production path, standardizing MXNet model artifacts, training configurations, and SageMaker deployment patterns to reduce handoff friction between data science and engineering teams. The deployment using Apache MXNet improved tagging speed and accuracy for brand customers while consolidating model lifecycle steps into a repeatable AWS and SageMaker based pipeline.

EagleView Technologies

Professional Services

1000

$140M

United States

Apache Software

Apache MXNet

AI Frameworks and Libraries

2018

n/a

In 2018, EagleView Technologies implemented Apache MXNet Category "" to build deep learning models that analyze aerial and satellite imagery for rapid property damage assessment for insurance clients. The deployment focused on automated image classification and per address damage scoring workflows to enable assessments within 24 hours after disasters. Apache MXNet was used to train convolutional neural network models and deploy them into a production inference pipeline, leveraging GPU accelerated training on cloud infrastructure. The implementation included data preprocessing, model training, evaluation, and model level scoring capabilities to produce per address damage assessments. The US based deployment scaled on AWS GPU instances and used Amazon ECS for production inference, instrumenting GPU backed containers for real time batch processing of post event imagery. Operational coverage centered on insurance claims and property assessment teams, with production inference integrated into EagleView operational pipelines for rapid turnaround. Governance emphasized model evaluation and comparability to human adjusters, the project reported model level accuracy comparable to human adjusters, for example about 96% per address accuracy in a published case. The architecture combined Apache MXNet with cloud GPU scaling and containerized inference on ECS to support time sensitive insurance workflows.

Nexity

Construction and Real Estate

3562

$3.9B

France

Apache Software

Apache MXNet

AI Frameworks and Libraries

2020

n/a

In 2020, Nexity used Apache MXNet and the GluonCV toolkit in a France-based internal hackathon to build a pose-detection proof-of-concept that automatically tallied show-of-hand votes during meetings. The implementation leveraged Apache MXNet for model definition and GluonCV for off-the-shelf computer vision operators, framing the project explicitly as a research PoC to evaluate operational CV use cases within the real-estate and facilities context. The PoC implemented a pose-detection workflow that combined model training and inference pipelines, relying on GluonCV model components for key capabilities such as keypoint detection and skeleton estimation. Apache MXNet served as the core machine learning framework for configuring the models and running inference, with limited tuning during the hackathon timeframe, and the team reported roughly a 70% success rate without heavy optimization. Operationally the PoC ran on Amazon SageMaker, using SageMaker to host training and inference experiments at the hackathon scale, and the solution was scoped to internal meeting environments in France. Governance remained at an experimental PoC level with outcomes used to assess applicability, and the exercise demonstrated Apache MXNet applicability for real-estate operational computer-vision use cases without implying production rollout or replacement of any named prior system.

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