Simpler workflow tools enable the rapid deployment of models

[A version os this post appears on the O’Reilly Strata blog.] Data science often depends on data pipelines, that involve acquiring, transforming, and loading data. (If you’re fortunate most of the data you need is already in usable form.) Data needs to be assembled and wrangled, before it can be visualized and analyzed. Many companiesContinue reading “Simpler workflow tools enable the rapid deployment of models”

Python data tools just keep getting better

[A version of this post appeared on the O’Reilly Strata blog.] Here are a few observations inspired by conversations I had during the just concluded PyData conference1. The Python data community is well-organized: Besides conferences (PyData, SciPy, EuroSciPy), there is a new non-profit (NumFOCUS) dedicated to supporting scientific computing and data analytics projects. The listContinue reading “Python data tools just keep getting better”

Data Science Tools: Fast, easy to use, and scalable

[A version of this post appears on the O’Reilly Strata blog.] Here are a few observations based on conversations I had during the just concluded Strata Santa Clara conference. Spark is attracting attention I’ve written numerous times about components of the Berkeley Data Analytics Stack (Spark, Shark, MLbase). Two Spark-related sessions at Strata were packedContinue reading “Data Science Tools: Fast, easy to use, and scalable”

MLbase: Scalable Machine-learning made accessible

[Cross-posted on the O’Reilly Strata blog.] In the course of applying machine-learning against large data sets, data scientists face a few pain points. They need to tune and compare several suitable algorithms – a process that may involve having to configure a hodgepodge of tools, requiring different input files, programming languages, and interfaces. Some softwareContinue reading “MLbase: Scalable Machine-learning made accessible”