Next-generation data science: Acceleration for team productivity

Data science can be time-consuming work. Increasingly, data scientists focus on the nitty-gritty details of building repeatable artifacts—for example, machine-learning and other statistical models—for deployment within always-on environments.Productivity pipeline Accelerating the pipeline of data-science development, deployment and optimization tasks is fundamental to team productivity. Several key pipeline tasks need to be accelerated:  Discovery and acquisition of data from diverse data lakes, big data clusters, cloud data services and other data stores  Accelerated ingestion and analysis of new data types—especially the image, audio and video content that is so fundamental to the streaming media and cognitive computing revolutions—through sample pipelines for computer vision and speech as well as data loaders for other data types  Prototyping, programming and modeling of data applications in Apache Spark, R, Python and other languages for execution within in-memory,…


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