Track: Hands-on Codelabs & Speakers Office Hours

Location: Mission

Day of week: Tuesday

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)

10:40am - 10:50am

IBM Watson

JeanCarl Bisson, Developer Advocate @IBM
Yacine Rezgui, Developer Advocate @IBM
Ross Cruickshank, Developer Advocate @IBM

10:40am - 10:50am

Create Cognitive Retail Chatbot

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.

CASE STUDY TALK (50 MIN)

11:00am - 11:50am

IBM Watson Codelabs Speakers Office Hours [10:50am - 12:50pm]

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.

Yacine Rezgui, Developer Advocate @IBM
JeanCarl Bisson, Developer Advocate @IBM
Ross Cruickshank, Developer Advocate @IBM
SHORT TALK (10 MIN)

12:50pm - 1:00pm

[CANCELED] Go for ML/AI

Daniel Whitenack, Data Scientist, Lead Developer Advocate @pachydermIO
CASE STUDY TALK (50 MIN)

1:10pm - 2:00pm

[CANCELED] Docker-izing your Data Science Applications Codelab

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.

Michael Masters,
Daniel Whitenack, Data Scientist, Lead Developer Advocate @pachydermIO
SHORT TALK (10 MIN)

2:25pm - 2:35pm

Tooling & Setup for My Neural Network

Martin Gorner, Parallel processing and machine learning @Google
CASE STUDY TALK (50 MIN)

2:45pm - 3:35pm

Tensorflow without a PhD 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

2:45pm - 3:35pm

iOS CoreML & Watson Visual Recognition

Classify images using IBM Watson Visual Recognition on device and offline using the latest CoreML APIs on iOS with Swift.

Link of the codelab

Yacine Rezgui, Developer Advocate @IBM

2:45pm - 3:35pm

Tensorflow Recurrent Neural Network Codelab 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

2:45pm - 3:35pm

Conversational Robot

Train TJBot, a Raspberry Pi-powered robot, to listen to natural language, understand intents and entities, and speak a response. In this exercise, use the IBM Watson Speech-to-Text service to transcribe audio into text, process it using the Watson Assistant service, and speak out a response to a question using the Watson Text-to-Speech service. The example Assistant workspace enables TJBot to share a little bit about this open-source project.
Link of the codelab

JeanCarl Bisson, Developer Advocate @IBM
CASE STUDY TALK (50 MIN)

2:45pm - 3:35pm

TensorFlow without a PhD 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.

Martin Gorner, Parallel processing and machine learning @Google

2:45pm - 3:35pm

AI Benefits for the Lazy Hacker

AI Benefits for the Lazy Hacker: Get fast usable access to AI services with Node-RED

The purpose of this code lab is to give you quick and easy access to the IBM Watson Cognitive Services APIs and allow you to experiment with image classification, speech to text and text to speech, document discovery, language identification and and translation with little or no code, and more.
Link of the codelab

Ross Cruickshank, Developer Advocate @IBM
SHORT TALK (10 MIN)

4:00pm - 4:10pm

What One Should Know About Spark MLlib

Hien Luu, Engineering Manager @Linkedin focused on Big Data
CASE STUDY TALK (50 MIN)

4:20pm - 5:10pm

End to End ML Without a Data Scientist

Machine Learning is super cool, but what about those of us who maybe got a D in statistics (or maybe didn't bother taking the class). With modern systems, it's relatively simple to train a model regardless of your background, but how do you know if the model you've trained does the "right" thing and how do you actually use your model? This talk will explore how to train models (using big data because that's what the presenter works with, but it will work just fine on small data as well), and how to serve them. We'll then talk about basic validation techniques, why you should A/B test, and the importance of keeping your models up to date (the world & humans keep _changing_ right after we've fit our models, its very frustrating).

Despite how fun deep learning is, this talk will focus on more easily explainable & spot checked models, like linear regression and decision trees.

Holden Karau, Spark Committer & Open Source Developer Advocate

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.