Machine learning, once implemented, tends to be specific to the data and requirements of the task at hand. Transfer learning is the act of abstracting and reusing those smarts

A cloud distributed data mesh supports a growing movement to decentralize data.

Fast, flexible, and developer-friendly, Apache Spark is the leading platform for large-scale SQL, batch processing, stream processing, and machine learning

Reinforcement learning is well-suited for autonomous decision-making where supervised learning or unsupervised learning techniques alone can’t do the job

Learn how the Cloudera, Hortonworks, and MapR data platforms are evolving to meet the demands for real-time analytics and machine learning

When relationships between entities are more important than the entities themselves, you have a business problem made for graph analysis

New frameworks Google GPipe and Microsoft PipeDream join Uber Horovod in distributed training for deep learning

By pre-processing or post-processing data, or even setting datasets to expire, humans can step in to correct machine learning models

Sharing a container environment and Nvidia GPU server has enabled Domino’s data scientists to create more complex and accurate models to improve store and delivery operations

SQL databases have constraints on data types and consistency. NoSQL does away with them for the sake of speed, flexibility, and scale