Enabling Reliable Data science and ML Projects

By my own experience, agreed.  Enterprise Data Science is rarely done very carefully, leading it open to error.

Enabling reliable, secure collaboration on data science and machine learning projects    A conversation with Paul Taylor, chief architect in Watson Data and AI, and IBM fellow.

By Frank Kane   O'Reilly Conference Jupyter

Machine learning researchers often prototype new ideas using Jupyter, Scala, or R Studio notebooks, which is a great way for individuals to experiment and share their results. But in an enterprise setting, individuals cannot work in isolation—many developers, perhaps from different departments, need to collaborate on projects simultaneously, and securely. I recently spoke with IBM’s Paul Taylor to find out how IBM Watson Studio is scaling machine learning to enterprise-level, collaborative projects.

First, a bit of background about Taylor. He has enjoyed a distinguished career at IBM over the past 17 years, where he started off working on Db2 and Informix, and working with big data and unstructured data well before those fields exploded. He has held many titles working in different technology areas as a distinguished engineer, chief architect, master inventor, CTO, and this year was appointed as an IBM Fellow.

Today, Taylor leads the technology of IBM Watson data and AI components, where he is exploring the convergence of data, AI, and public cloud with IBM Watson Studio. Watson Studio provides a suite of tools for data scientists, application developers, and subject matter experts to collaborate and work with data to conduct analytics and data science, and to build, train, and deploy models at scale.

Frank Kane: Why is better collaboration in data science important? What sorts of opportunities do you see it creating for real-world developers and businesses?

Paul Taylor: A lot of times I go in to talk to C-suite folks who are running the data science teams. They're in a real challenge because, traditionally, many of those clients and the scientists are using their own little tools, and they may be very sophisticated tools, or they may be very naïve ones. They're all working in silos, and they're using their own tools in their own way.   ... "