Neptune.ai Case Studies Hypefactors: Enhancing Media Intelligence with IoT and Machine Learning

Edit This Case Study Record

	Hypefactors: Enhancing Media Intelligence with IoT and Machine Learning Neptune.ai

Hypefactors: Enhancing Media Intelligence with IoT and Machine Learning

Neptune.ai

Technology Category	Analytics & Modeling - Computer Vision Software Analytics & Modeling - Machine Learning
Applicable Industries	Buildings Equipment & Machinery
Use Cases	Computer Vision Retail Store Automation
Services	Training
Challenge	Hypefactors, a technology company specializing in media intelligence and reputation tracking, faced a significant challenge in managing their data pipelines. These pipelines monitor a wide range of media, including social media, print, television, and radio, to analyze changes in their customers' brand reputation. The process involves gathering data from various sources and enriching it with machine learning (ML) features. However, as the company expanded its operations and started working on more complex ML problems, they encountered difficulties in tracking their experiments. Initially, the team used Slack for collaboration and personal notes/files for storing training metadata and model artifacts. However, as the number of models, features, and team members increased, this method became inefficient and created structural bottlenecks. Read More
About Customer	Hypefactors is a technology company that operates in the media intelligence and reputation tracking domain. They offer a machine learning-based Public Relations (PR) automation platform, which includes all the tools necessary to power the PR workflow and visualize the results. Hypefactors' data pipelines monitor a wide range of media, from social media to print media, television, and radio, to analyze changes in their customers' brand reputation. The company works on a variety of machine learning problems, including natural language processing classification, computer vision, and regression for business metrics, to enrich the data they gather. Read More
Solution	Recognizing the need for a more efficient tool to manage their growing needs, Hypefactors explored various options and eventually chose Neptune. Neptune was selected for its cost-benefit structure, which was particularly appealing to Hypefactors as they only occasionally run experiments and did not want to pay for a tool when not in use. Neptune's approach aligns with Hypefactors' requirements and value proposition, understanding that every team has a unique approach and cannot be confined to a one-dimensional pricing structure. With Neptune, all metadata and model artifacts are now stored in a single place, making accessibility and research much easier. Additionally, Neptune has facilitated smoother collaboration among team members on projects. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Computer Vision Software

Analytics & Modeling - Machine Learning

Applicable Industries

Buildings

Equipment & Machinery

Use Cases

Computer Vision

Retail Store Automation

Services

Training

Challenge

Hypefactors, a technology company specializing in media intelligence and reputation tracking, faced a significant challenge in managing their data pipelines. These pipelines monitor a wide range of media, including social media, print, television, and radio, to analyze changes in their customers' brand reputation. The process involves gathering data from various sources and enriching it with machine learning (ML) features. However, as the company expanded its operations and started working on more complex ML problems, they encountered difficulties in tracking their experiments. Initially, the team used Slack for collaboration and personal notes/files for storing training metadata and model artifacts. However, as the number of models, features, and team members increased, this method became inefficient and created structural bottlenecks.

About Customer

Hypefactors is a technology company that operates in the media intelligence and reputation tracking domain. They offer a machine learning-based Public Relations (PR) automation platform, which includes all the tools necessary to power the PR workflow and visualize the results. Hypefactors' data pipelines monitor a wide range of media, from social media to print media, television, and radio, to analyze changes in their customers' brand reputation. The company works on a variety of machine learning problems, including natural language processing classification, computer vision, and regression for business metrics, to enrich the data they gather.

Solution

Recognizing the need for a more efficient tool to manage their growing needs, Hypefactors explored various options and eventually chose Neptune. Neptune was selected for its cost-benefit structure, which was particularly appealing to Hypefactors as they only occasionally run experiments and did not want to pay for a tool when not in use. Neptune's approach aligns with Hypefactors' requirements and value proposition, understanding that every team has a unique approach and cannot be confined to a one-dimensional pricing structure. With Neptune, all metadata and model artifacts are now stored in a single place, making accessibility and research much easier. Additionally, Neptune has facilitated smoother collaboration among team members on projects.

Impact #1	The implementation of Neptune as a tool for experiment tracking has significantly improved Hypefactors' workflow. All metadata and model artifacts are now centralized in one place, making it easier for the team to access and research necessary information. This has also eliminated the structural bottlenecks that were previously caused by individual team members storing data in their respective systems. Furthermore, Neptune has facilitated smoother collaboration among team members on projects, enhancing the overall efficiency of the team.

Impact #1

The implementation of Neptune as a tool for experiment tracking has significantly improved Hypefactors' workflow. All metadata and model artifacts are now centralized in one place, making it easier for the team to access and research necessary information. This has also eliminated the structural bottlenecks that were previously caused by individual team members storing data in their respective systems. Furthermore, Neptune has facilitated smoother collaboration among team members on projects, enhancing the overall efficiency of the team.

Download PDF Version

Overview

Hypefactors: Enhancing Media Intelligence with IoT and Machine Learning

Operational Impact