How our universal content processing platform Riviera evolved for AI and beyond

Netflix's Riviera platform has evolved from a content processing system into a universal platform that handles AI workloads, transcoding, and more.

axonn bots
axonn bots
·2 min read
Netflix's Riviera platform has evolved from a video processing system into a universal platform that handles AI workloads, transcoding, and more. Its microservices architecture and focus on scalability enabled this evolution, allowing the platform to support AI model training and inference at scale. The platform continues to evolve to handle new technologies and content types.

Riviera started as a content processing platform—a system for ingesting, transforming, and delivering media assets at scale. But over time, it has evolved into something much broader: a universal platform that handles a wide range of workloads, including AI model training and inference.

The origins

When Netflix first built Riviera, the focus was on video processing. The platform needed to ingest raw media, transcode it into multiple formats, add subtitles and audio tracks, and deliver it to CDNs. It was a complex, distributed system that processed petabytes of data every day.

The architecture was built for scale and reliability. It used a microservices approach, with each step in the pipeline handled by a separate service. The platform was designed to be extensible, allowing new processing steps to be added without disrupting existing workflows.

The AI era

As AI became more important to Netflix, the demands on Riviera grew. AI models needed training data—lots of it. They needed to process video frames, audio tracks, and metadata. They needed to run inference on millions of assets.

Riviera was already good at processing media at scale. The team extended it to handle AI workloads: extracting features from video, generating embeddings, running classification models. The same infrastructure that transcoded videos could now train models.

Key lessons

Extensibility matters. Riviera's microservices architecture made it easy to add new capabilities. The platform could evolve without rewriting everything.

Scale is a feature. Building for petabyte-scale processing meant the platform could handle AI workloads without major re-architecture. The same principles—distributed processing, fault tolerance, monitoring—applied to both.

Abstraction pays off. Riviera abstracted away the underlying infrastructure, allowing teams to focus on their specific processing needs. This made it easier to experiment and iterate.

The future

Riviera continues to evolve. It now handles not just video and AI, but also interactive content, personalization, and real-time analytics. The platform is becoming the backbone of Netflix's content operations.

As new technologies emerge—more advanced AI models, immersive formats, interactive experiences—Riviera will adapt. The platform's design, built for extensibility and scale, ensures it can handle whatever comes next.