Track: Hands-on Codelabs & Speakers Office Hours

Location: Mission

Day of week: Wednesday

Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator. The idea is to give attendees hands on experience using the tools and techniques of data science and then provide a space to have the questions generated from working with the tool answered. We'll provide an environment for developers to get up and running quickly on a product, walk through a series of steps.

Track Host: Wes Reisz

Software/Technical Advisor C4Media & QCon Chair, previous Architect @HPE

Wes Reisz joined QCon in 2015 and leads QCon Editorial as the conference chair. Wes focuses his energies on providing a platform for practicing engineers to tell their war stories so innovative/early adopter stage engineers can learn, adopt, and, in many cases, challenge each other. Before joining the QCon Team, Wes held a variety of enterprise architecture and software development roles with HP. His focus with HP was around developing/federating identity, integration/development of Java stack applications, architecting portal/CM solutions, and delivering on mobility in places like US Army’s Human Resources Command (HRC), Army Recruiting Command, and Army Cadet Support Program. In 2002, Wes began teaching as an adjunct faculty member at the University of Louisville. He continues to teach 400-level web architecture and mobile development courses to undergraduates. He is currently teaching Mobile Application Development with Android.

SHORT TALK (10 MIN)

9:00am - 9:10am

The Case for R for AI developers

David Smith, Cloud Developer Advocate @Microsoft
CASE STUDY TALK (50 MIN)

9:20am - 10:10am

R for AI developers Speaker Office Hours

In this code lab, we'll use Azure Notebooks to explore various statistical analysis with R, and discuss how they can be applied to AI applications. No R knowledge is required; you'll get to experience R by running pre-prepared code and data within the Notebook environment, and ask questions and explore further during the Office Hour.

David Smith, Cloud Developer Advocate @Microsoft
SHORT TALK (10 MIN)

10:35am - 10:45am

Very Large Datasets With the GPU Data Frame

Veda Shankar, Senior Developer Advocate @MapD
CASE STUDY TALK (50 MIN)

10:55am - 11:45am

Gimel Codelab Speakers Office Hours

Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator or person that can answer specific questions. The idea is to give attendees hands on experience using the tools and techniques of data science and then provide a space to have the questions generated from working with the tool answered. We'll provide an environment (or sandboxed quickstart) for developers to get up and running quickly on a product, walk through a series of steps.

Anisha Nainani, Software Engineer @Paypal
Dheeraj Rampally, Senior Software Engineer @Paypal
SHORT TALK (10 MIN)

12:45pm - 12:55pm

TensorBoard: Visualizing Learning

Chi Zeng, Software Engineer @Google
CASE STUDY TALK (50 MIN)

1:05pm - 1:55pm

TensorBoard Codelab Speaker Office Hours

Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator. The idea is to give attendees hands on experience using the tools and techniques of data science and then provide a space to have the questions generated from working with the tool answered. We'll provide an environment for developers to get up and running quickly on a product, walk through a series of steps.

Chi Zeng, Software Engineer @Google
SHORT TALK (10 MIN)

2:20pm - 2:30pm

Continuous Delivery for AI Applications

Asif Khan, Tech Leader @AWS
CASE STUDY TALK (50 MIN)

2:40pm - 3:30pm

Google Dataflow Codelab Speaker Office Hours

Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator or person that can answer specific questions. The idea is to give attendees hands on experience using the tools and techniques of data science and then provide a space to have the questions generated from working with the tool answered. We'll provide an environment (or sandboxed quickstart) for developers to get up and running quickly on a product, walk through a series of steps.

Martin Gorner, Parallel processing and machine learning @Google

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.