Preview all research reports and prototypes
Our research focuses on emerging data and machine learning-enabled trends. Accompanying the reports are working prototypes that exhibit the capabilities of the algorithm and offer detailed technical advice on its practical application.
Semantic recommendations
In this report, we show how using the semantic content of items can help solve common recommendation pitfalls such as the cold start problem, and open up new product possibilities.
Interpretability
In this report, we show how to make models interpretable without sacrificing their capabilities or accuracy.
Probabilistic programming
Here, we show how to use probabilistic programming and Bayesian inference to easily build tools that make better predictions for more effective decision making.
Summarization
Learn how to use deep learning and embeddings to make text computable for a variety of business applications and products.
Deep learning: Image analysis
This report explores the history and current state of deep learning, explains how to apply it, and predicts future developments.
Probabilistic methods for realtime streams
Here, we explore probabilistic methods that offer highly efficient models for extracting value from streams of data as they are generated.
Natural language generation
In this report, we look at how machine systems can turn highly structured data into human language narrative.
Read the Fast Forward Labs blog
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Introducing SciFi
Today we are launching a mini-site featuring our collection of short stories inspired by new develop...
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PyTorch for Recommenders 101
Recommenders, generally associated with e-commerce, sift though a huge inventory of available items ...
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Join the Machine Learning Team at Cloudera!
At Cloudera, we believe that with data we can make what is impossible today possible tomorrow. We ar...
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