At Firemind, we’ve seen firsthand the transformative power of AWS machine learning services in the media and entertainment industry. In this article, we will explore how AWS machine learning is transforming media and entertainment, as well as providing some real-world examples of how media and entertainment companies are leveraging these technologies.

AWS offers a range of machine learning services that can be used by media and entertainment companies, including Amazon SageMaker, Amazon Rekognition, and Amazon Transcribe. These services can help companies to build intelligent applications that can analyse video and audio content, recognise faces and objects, and transcribe speech to text.

Amazon SageMaker is a fully managed service that provides developers and data scientists with tools to build, train, and deploy machine learning models. SageMaker can be used to build models for a range of media and entertainment use cases, such as content recommendation engines, sentiment analysis, and video analysis.

Amazon Rekognition is a deep learning-based image and video analysis service that can identify objects, people, text, and activities in video content. Rekognition can be used to automate video indexing, identify faces in images and videos, and analyse video content for sentiment analysis.

Amazon Transcribe is a speech-to-text service that uses machine learning to convert audio and video files to text. Transcribe can be used to automatically generate captions for video content, transcribe interviews and speeches, and provide subtitles for international audiences.

Let’s take a look at some real-world examples of how media and entertainment companies are using AWS machine learning services.

Discovery Communications

Discovery Communications is a global media company that owns and operates a range of television networks, including Discovery Channel, Animal Planet, and TLC. To improve content discovery for its viewers, Discovery Communications uses Amazon SageMaker to build a personalised content recommendation engine.

The recommendation engine uses machine learning algorithms to analyze viewers’ viewing histories and provide personalised recommendations for new content. The engine is powered by AWS services, including Amazon S3 for data storage and Amazon EC2 for computing resources.

As a result of implementing the recommendation engine, Discovery Communications saw a 60% increase in video views per visitor, and a 25% increase in the click-through rate on recommended content.

NBC Universal

NBC Universal is a media and entertainment company that owns and operates a range of television networks, including NBC, CNBC, and USA Network. To automate the process of captioning video content for its international audiences, NBC Universal used Amazon Transcribe.

Using Transcribe, NBC Universal was able to automatically generate accurate captions for its video content, reducing the time and cost associated with manual captioning. The captions were also of a higher quality, as they were generated using machine learning algorithms.

As a result of using Amazon Transcribe, NBC Universal was able to reduce the time and cost of captioning its video content by 80%.

MGM Studios

MGM Studios is a film and television studio that produces and distributes a range of movies and TV shows. To automate the process of video indexing and content discovery, MGM Studios used Amazon Rekognition.

Using Rekognition, MGM Studios was able to automatically index its video content based on the objects, people, and activities in each video. This allowed the studio to quickly search and find specific video content, and to analyze video content for sentiment analysis.

As a result of using Amazon Rekognition, MGM Studios was able to reduce the time and cost of video indexing and content discovery by 50%.

If you’re looking to transform your use of machine learning, with enhanced usability of your data and analytics, reach out to our team. As Machine Learning Competency partners with AWS, we have the specialised team, ready to work on your next media and entertainment ML project.