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An updated Shiny testing package and new Shiny framework were featured at this week's Shiny conference.
Recording the model development process on the blockchain can make that process more structured, transparent, and repeatable, resulting in less bias and more accountability.
Artificial general intelligence will be able to understand or learn any intellectual task that a human can. AGI will have high costs and huge risks, but it’s coming—maybe soon.
Aerospike Database 6 release also introduces massively parallel secondary indexes, which promise the same speed and efficiency as primary indexes.
Dive into data lakes—what they are, how they're used, and how data lakes are both different and complementary to data warehouses.
Databricks Lakehouse Platform combines cost-effective data storage with machine learning and data analytics, and it's available on AWS, Azure, and GCP. Could it be an affordable alternative for your data warehouse needs?
Data scientists and machine learning scientists have similar roles, but a machine learning scientist specializes in researching and implementing complex algorithms.
Today we’re seeing a major evolution in how search anticipates what users want before they know they are looking for it. Developers should be tuning in.
Neo4j's new managed service works with the company’s cloud-based graph database offering, AuraDB, providing a library of graph algorithms, machine learning pipelines, and data science methodologies for data scientists and developers.
Understand the two dimensions of scaling for database query and ingest workloads, and how sharding can make scaling elastic—or not.