An archive of my speaking engagements with talk summaries, links to slide decks and recordings…

Data Science Community Day

July 24, 2018

Spend your day doing data science, not reading Python docs :
Building visualization apps in Jupyter notebooks with Pixiedust in IBM Watson Studio

The Jupyter notebook has quickly become one of data scientists’ favorite tools. When using them in IBM’s Watson Studio, you get a complete platform for building an application – from data preparation and analytics to building and deploying machine learning models. Jupyter notebooks are a big step up from executing code at the command line, but the basic notebook environment doesn’t do much to automate repetitive tasks.

This is where Pixiedust comes in. It puts some of the most common visualization tasks behind a convenient GUI so you don’t have to remember all those obscure arguments that go into the creation of a simple bar chart. Even better, Pixiedust is extensible, so if the function you want to automate isn’t available, you can write a “PixieApp” – a Python class that extends Pixiedust – to do the job. Come learn how to use Pixiedust and build PixieApps.


Mid-Atlantic Developer conference

July 13-14, 2018

Managing an autonomous bus transit network with open source

Earlier this year IBM showed a vision of the future of autonomous bus transit networks at the Consumer Electronics Show. The exhibit featured a real Olli bus and a bus stop that adapted to the accessibility needs of the rider. Whether you are young or old, blind or deaf, cognitively impaired, wheelchair-bound or color-blind, the intelligent Olli bus stop adapts to your needs and makes it easy to call for a bus, know when it will arrive, find points of interest near your destination, and get on and off safely.

Making all this happen requires a lot of data and smart processing. Buses reported their location every 500 milliseconds. We tracked riders entering and exiting the stop and the bus. We also track the health of the IoT systems that keep everything running. And it was built around Apache CouchDB being the source of truth for information events, and all other software listening to the CouchDB changes feed and deciding whether to do something based on the change. In this presentation I’ll discuss how we built the bus stop “concierge” kiosk app using a host of open source technologies, including CouchDB, PouchDB, React, Redux and Mapbox GL.


ODSC East

May 2018

Formalizing Reusable data science: building apps in Jupyter notebooks in Watson Studio with Pixiedust

The Jupyter notebook has quickly become one of data scientists’ favorite tools. When used within Watson Studio, you get a complete platform for building an application – from data preparation and analytics to building and deploying machine learning models. Jupyter notebook is It’s a big step up from executing code at the command line, but the basic notebook environment doesn’t do much to automate repetitive tasks. This is where Pixiedust comes in. It puts some of the most common visualization tasks behind a convenient GUI so you don’t have to remember all those obscure arguments that go into the creation of a simple bar chart. Even better, Pixiedust is extensible, so if the function you want to automate isn’t available, you can write a “PixieApp” – a Python class that extends Pixiedust – to do the job. Come learn how to use Pixiedust and build PixieApps, and also get a sneak peek at a new PixieApp for doing basic data cleaning and exploration we’ve been working on.

Recording: https://www.youtube.com/watch?v=SyIUILk1_JQ&feature=youtu.be

Predicting NBA winners with IBM Watson Machine Learning
Managing data, building a machine learning model, and deploying it as an API ready to be used in a Web app is so easy to do on the IBM Cloud I’ll show you how in 5 minutes! We’ll do it all in a Jupyter Notebook in Watson Studio, IBM’s born-on-the-cloud data science platform.