Solution Accelerators for Communication Service Providers

Solution accelerators for communication service providers

Based on best practices from our work with the leading brands, we’ve developed solution accelerators for common use cases to save weeks or months of development time for your data engineers and data scientists.

Quality of service

Providing consistent quality in the streaming video experience is table stakes to keep fickle audiences with ample entertainment options on your platform. This solution is a quick start for most streaming video platform environments to embed a QoS real-time streaming analytics solution.

Customer Lifetime Value Model

Understanding and identifying who your most valuable customers are will help guide better marketing investment and product development choices. This solution focuses on retention and spending components to then combine into an overall CLV model ideal for transaction-based business like TVOD or AVOD.



Subscriber Churn Prediction

Identify customer behaviors to predict the increased risk of subscription cancellation using Kaplan-Meier curves and Cox Proportional Hazard models.



Scaling ML models for IoT

In order to train machine learning models on real-time data coming from an IoT sensors, some use cases require each connected device to have its own individual model since many basic machine learning algorithms often outperform a single complex model. However, this can lead to IoT and per-device data so large that it won’t fit on any one machine, per-device data does fit on a single machine. Additionally, the data science team is implementing using single node libraries like sklearn and pandas, so they need low friction in distributing their single-machine proof of concept. In this blog, we demonstrate how you solve this problem with two distinct schemes for each IoT device: Model Training and Model Scoring.

Predictive maintenance

Maintaining assets such as compressors is an extremely complex endeavor: they are used in everything from small drilling rigs to deep-water platforms, the assets are located across the globe, and they generate terabytes of data daily. A failure for just one of these compressors costs millions of dollars of lost production per day. In this solution, we teach you how to build an end-to-end predictive data pipeline that can provide a real-time database to maintain asset parts and sensor mappings, support a continuous application that processes a massive amount of telemetry, and allows you to predict compressor failures against these datasets.

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