tensorflow

Panel: Career Advice for Data Scientists | RStudio (2020)

Featuring: Gabriela de Queiroz, David Keyes, Sydeaka Watson, and Jen Hecht

This panel will be focused on how you build a career around R! Our panelists are all passionate about R and have each taken a different path to build a career around that passion. We’ll be touching on topics like the different stages of career growth and how you find a community you can go to for support. If you’re just getting started in your career, looking to make a transition, or interested in learning some great career-building skills, be sure to join us!

About Gabriela de Queiroz: Gabriela de Queiroz is a Sr. Engineering & Data Science Manager at IBM where she manages and leads a team of developers working on Data & AI Open Source projects. Her team contributes to projects such as TensorFlow, PyTorch, Apache Arrow, Apache Spark, and several other open source projects inside IBM. She works to democratize AI by building tools and launching new open source projects. She is passionate about making data science available to everybody and is actively involved with several organizations to foster an inclusive community. She is the founder of AI Inclusive, a global organization that is helping increase the representation and participation of gender minorities in Artificial Intelligence. She is also the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 180 chapters in 50+ countries.

About David Keyes: David Keyes is the founder of R for the Rest of Us, which teaches people and individuals to use R through online courses, custom trainings, and public workshops. With stops as an elementary school teacher, PhD social scientist, and program evaluator, David brings his unconventional career trajectory to his current role, helping those with who don’t think of themselves as typical R users embrace the power of R.

About Sydeaka Watson: Dr. Sydeaka Watson is a native of New Orleans, Louisiana and currently lives in Dallas, Texas. She is Founder and Owner of Korelasi Data Insights, LLC and a Senior Data Scientist at Elicit Insights, LLC. In these roles, Sydeaka uses predictive analytics and visual tools to draw insights from diverse datasets. Sydeaka earned a Ph.D. in Statistics from Baylor University and has several years of teaching experience. In her 5 years as Research Assistant Professor in The University of Chicago Biostatistics Laboratory, she consulted with over 110 biomedical research teams in The University of Chicago Medical Center, specializing in statistical analysis and experimental design for clinical research studies. Her current research interests include applications of (1) image recognition for computer vision and (2) data science for social justice. Sydeaka currently serves as Organizer of the R-Ladies Dallas Chapter. She also volunteers in the Dallas chapters of Girls Who Code and Black Girls Code.

About Jen Hecht: Jen Hecht is the VP of People Operations at RStudio. She was first introduced to R in 2013, as a non-programmer seeking better ways to manage analytical projects - a quest which was aided both by the RStudio toolchain and the welcoming support of R Ladies, R meetup groups, and other wonderful open resources. Ever since, she has been captivated by open data science tools and the communities that build them. Before joining RStudio in 2018, Jen held HR and People Analytics roles in a variety of industries, including financial services, biotech, and used record shops. Outside of work, Jen loves books and music of all kinds, and is a novice fly caster

Heather & Jacqueline Nolis | Push straight to prod: API development with R and Tensorflow | RStudio

Talk from rstudio::conf(2019)

When tasked with creating the first customer-facing machine learning model at T-Mobile, we were faced with a conundrum. We had been told time and time again to deploy machine learning models in production you had to use Python, but our very best data scientists were fluent in building neural networks in R with Keras and TensorFlow. Determined to avoid double work, we decided to use R in production for our machine learning models. After months of work, wrangling our containers to meet cloud security compliance, and conforming to DevOps standards, we succeeded in creating a containerized API solution using the keras and plumber R packages and Docker. Today R is actively powering tools that our customers directly interact with and we have open sourced our methods. In this talk, we’ll walk through how to deploy R models as container-based APIs, the struggles and triumphs we’ve had using R in production, and how you can design your teams to optimize for this sort of innovation.

About Heather Nolis: Heather Nolis is a founding member of the AI @ T-Mobile team, focusing the conversion of cutting-edge analyses to real-time, scalable data-driven products. She began her career in neuroscience but once realized how heavily that field relied on software built by other people, she pivoted - deciding to make software herself. You can find her @heatherklus on Twitter, where she speaks about diversity in technology, the ethical implications of data, and cats.

About Jacqueline Nolis: Dr. Jacqueline Nolis is a co-founder of Nolis, LLC, a data science consulting firm. She has over a decade of experience using data to help companies including DSW, Union Bank, Microsoft, and Airbnb. She has a PhD from Arizona State University where her research focused on electric vehicle route optimization. For fun she likes to use machine learning for humor

Dr. Sydeaka Watson | Neural Networks for Longitudinal Data Analysis | RStudio (2020)

Longitudinal data (or panel data) arise when observations are recorded on the same individuals at multiple points in time. For example, a longitudinal baseball study might track individual player characteristics (team affiliation, age, height, weight, etc.) and outcomes (batting average, stolen bases, runs, strikeouts, etc.) over multiple seasons, where the number of seasons could vary across players. Neural network frameworks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) can flexibly accommodate this data structure while preserving and exploiting temporal relationships. In this presentation, we highlight the use of neural networks for longitudinal data analysis with tensorflow and keras in R

Daniel Falbel | What’s new in TensorFlow for R | RStudio (2020)

TensorFlow is the most popular open-source platform for machine learning and it’s ecosystem is evolving incredibly fast. In this talk we will explore what’s new in TensorFlow 2.0 as well as how to build data pre-processing pipelines using the tfdatasets package and how to use pre-trained models with tfhub

Daniel Falbel