RTL Nederlands Relies on Pachyderm’s Scalable, Data-Driven Machine Learning Pipeline to Make Broadcast Video Content More Discoverable
Customer Company Size
Large Corporate
Region
- Europe
Country
- Netherlands
Product
- Pachyderm
- Videoland
- RTL XL
Tech Stack
- Machine Learning
- Artificial Intelligence
- Natural Language Processing
- Cloud Computing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Cost Savings
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - API Integration & Management
Applicable Functions
- Discrete Manufacturing
- Product Research & Development
Use Cases
- Machine Condition Monitoring
- Process Control & Optimization
- Predictive Maintenance
Services
- Data Science Services
- Cloud Planning, Design & Implementation Services
About The Customer
RTL Nederlands is a 100% subsidiary of RTL Group, Europe’s largest TV, radio and production company. RTL Group is 75.1 percent owned by Bertelsmann, a large international media group. Sister companies of RTL Group are the publishers Penguin Random House and Gruner + Jahr, music company BMG and customer service provider Arvato. RTL Nederlands broadcasts to millions of daily TV viewers, along with delivering streaming content that garners hundreds of millions of monthly views online. Its parent, RTL Group, is Europe’s largest broadcaster and part of Bertelsmann, one of the world’s largest media conglomerates.
The Challenge
RTL Nederlands, part of Europe’s largest broadcast group, wanted to use artificial intelligence (AI) to make video content more valuable and discoverable for millions of subscribers. The company broadcasts to millions of daily TV viewers, along with delivering streaming content that garners hundreds of millions of monthly views online. One of the key growth metrics for RTL Nederlands is viewership, but optimizing the value and discoverability of video assets is an extremely labor-intensive endeavor. That makes it ripe for automation, and the team applied machine learning to optimize key aspects of its video platform, like creating thumbnails and trailers, picking the right thumbnail for those trailers, and inserting ad content into video streams.
The Solution
The team solved this problem by breaking complicated tasks into simpler subtasks, eliminating larger task-specific models in favor of an assembly of reusable modules. This modular approach to machine learning allowed the company to train the AI on various elements of the video stream across visual (frame extraction, shot segmentation, facial recognition), audio (tagging, speech identification, musical genre) and text (language detection, key phrases) subtasks. Pachyderm provides clear understanding of data lineage during model experimentation, giving Adarga’s data scientists the insight needed for traceability and reproducibility. This effectively creates a controlled environment for Adarga, allowing the team to quickly assess and understand model development.
Operational Impact
Quantitative Benefit
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