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How to Apply AI and Machine Learning to Media and Entertainment

If you are a content producer with large content libraries, how do you find the right content to serve your audience quickly?


Do you have an automated way to pull out the right content from your archive, or are you relying on manual labor to watch each video and add metadata for each asset?

As rich media content explodes – not just professionally produced long-form content, but also new digital-first content – metadata is key to describing and categorizing digital content to provide better search visibility. Metadata directly affects your ability to search and find content efficiently. The challenge faced by content producers today is that existing metadata is basic and limited to only include title, cast, and synopsis. The problem is worse when you have many years’ worth of content that has never been tagged with metadata. Even if you can hire people to view archival material and add metadata, the metadata content often lacks quality and accuracy.

What if you could enrich the metadata by tagging your content with relevant data such as theme, tone, mood, trending data, and identification of things like make of cars and specific people included in the videos? Better metadata can help content producers find content quickly and accurately, so that they can deliver the right entertainment to the right viewers at the right time. Better metadata can also guide you in personalizing your services, thereby optimizing your content for specific audiences.

What if you could automate metadata creation with AI? By applying machine learning to rapidly analyze vast amounts of unstructured content and determine meaningful metadata to record, you can make your searches more specific. This will significantly improve your ability to find the right content when you need it.

AI relies on data to be powerful. The more metadata you have, the more AI can do to optimize your content. This is where the MapR Data Platform can help. MapR provides effective AI tools to construct machine learning models and a robust data infrastructure behind them to enable content producers to connect millions of people everyday to the entertainment they love.

Content from entertainment and media companies are measured in TBs and PBs today. All this unstructured content can be stored and managed on the MapR Data Platform. And when you use AI tools from the MapR Data Science Refinery to automatically generate more meaningful metadata, you can store, process, and analyze all existing and new content and metadata in a single data platform.

You might ask: how does MapR help me generate “meaningful” metadata? With built-in replicable and replayable event streaming, you can feed real-time, relevant data from internal and external data sources, such as social media and trending data, to your machine learning models to enrich metadata for your content. To ensure business continuity, we can replicate metadata alongside data to allow producers to failover between locations. Machine learning models are only as good as the data they are trained on. With MapR, data scientists can get access to all the data and allow their models to continually learn at scale, which can effectively make content more discoverable. Data scientists within your organization can leverage the MapR Platform’s global namespace, snapshots, and replication capabilities to efficiently share and collaborate on AI projects.

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