Fernando Prez opens JupyterCon with an overview of Project Jupyter, describing how it fits into a vision of collaborative, community-based open development of tools applicable to research, education, and industry.

Alexandre Archambault explores why an official Scala kernel for Jupyter has yet to emerge. Part of the answer lies in the fact that there is no user-friendly, easy-to-use Scala shell in the console (i.e., no IPython for Scala). But there's a new contender, Ammonitealthough it still has to overcome a few challenges, not least being supporting by big data frameworks like Spark, Scio, and Scalding.

Data exploration usually entails making endless one-use exploratory plots. Victor Zabalza shares a Python package based on dask execution graphs and interactive visualization in Jupyter widgets built to overcome this drudge work. Victor offers an overview of the tool and explains how it was built and why it will become essential in the first steps of every data science project.

Ready to dip your toe into data science? Va Barbosa explains why you should start with notebooks and PixieDust, a new open source library that helps data scientists and developers working in the Jupyter Notebook and Apache Spark be more efficient.

A wide variety of open source frameworks and tools support artificial intelligence and deep learning. Adrian Cockcroft explains how AWS has packaged a number of themincluding deep learning frameworks such as Caffe, CNTK, Keras, MXNet, TensorFlow, Theano, and Torch and supporting tools like Jupyter and Anacondainto an Amazon Machine Image with optimized GPU support.

The Netflix data platform is constantly evolving, but fundamentally it's an all-cloud platform at a massive scale (40+ PB and over 700 billion new events per day) focused on empowering developers. Kurt Brown dives into the current technology landscape at Netflix and offers some thoughts on what the future holds.

Peng Du and Randy Wei offer an overview of Ubers data science workbench, which provides a central platform for data scientists to perform interactive data analysis through notebooks, share and collaborate on scripts, and publish results to dashboards and is seamlessly integrated with other Uber services, providing convenient features such as task scheduling, model publishing, and job monitoring.