Working with multiple open source projects and tools is common practice. Combining elements of popular projects, including Spark, Hadoop, GearPump, Cloud Foundry, and Docker, to form a new analytics platform brings unique challenges. Trusted Analytics Project (TAP) team members Chuck Freedman and Kyle Ambert share lessons learned during the project’s evolution from vision to launch and discuss the key aspects that are helping TAP grow as features are added, optimizations contributed, and a community spanning developers and data scientists gets supported and involved.
This session is sponsored by Intel & TAP.
Chuck Freedman is dedicated to driving platform success and adoption across all industries. An evangelist and inspirational force for a decade, he has engaged and enabled developers, designers, scientists, and engineers with a range of innovative technologies. Chuck applies his unique interest, vision, and experience with a genuine approach towards advocacy and evangelism. Over a 17-year technology career, he has led platform business strategy, marketing, and product development to help businesses drive innovation, partnerships, quality integration, and adoption. Chuck has authored books and articles that inspire use of platforms and engaged audiences at over 50 events and meetups around the world.
Kyle Ambert is lead data scientist at Intel’s Artificial Intelligence and Analytics Solutions group, where he uses machine learning and statistical methods to solve real-world big data problems. Currently, his research centers around novel applications of machine learning in the health and life sciences. Kyle contributes to the data science direction of the Trusted Analytics Platform, particularly as it pertains to analytical pipeline and algorithm development. He holds a BA in biological psychology from Wheaton College and a PhD in biomedical informatics from Oregon Health & Science University, where his research focused on text analytics and developing machine-learning optimization solutions for biocuration workflows in the neurosciences.
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