Mar 15–18, 2020

Interactive sessions

New: Interactive sessions (powered by Katacoda) give you the chance to manipulate technology in real time to discover how it works. You’ll input, edit, run code and render live results as you learn with guided instruction.

Tuesday, March 17

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11:00am12:30pm
Location: 230 A
Chris Fregly (Amazon Web Services)
Join in to build real-world, distributed machine learning (ML) pipelines with Chris Fregly using Kubeflow, MLflow, TensorFlow, Keras, and Apache Spark in a Kubernetes environment. Read more.
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11:00am12:30pm
Location: 210 D/H
Joseph Nelson (Roboflow)
Computer vision gives you the ability to make anything in the real world into read/write on your phone. Joseph Nelson walks you through the end-to-end flow required to train a model for mobile deployment, including image collection, preprocessing and augmenting considerations, model training, and saving the TensorFlow Lite (TFLite) model in an appropriate format for deployment. Read more.
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1:45pm3:15pm
Location: 230 A
Austin Bennett (Sling Media)
Austin Bennett offers hands-on training with the Apache Beam programming model. Beam is an open-source unified model for Batch and Stream data processing that runs on execution engines like Google Cloud Dataflow, Apache Flink, and Apache Spark. Read more.
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1:45pm3:15pm
Location: 210 D/H
Patrick Buehler (Microsoft)
In recent years, computer vision (CV) has seen quick growth in quality and usability, driving business adoption of AI solutions. Patrick Buehler offers a comprehensive introduction on deep learning models for computer vision (CV). And you'll get your hands dirty training and evaluating CV models with prepared examples and exercises. Read more.
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4:15pm5:45pm
Location: 230 A
Sarah Guido (InVision)
Getting your data ready for modeling is the essential first step in the machine learning process. Sarah Guido outlines the basics of preparing and standardizing data for use in machine learning models. Read more.

Wednesday, March 18

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11:00am12:30pm
Location: 230 A
Martin Frigaard (Aperture Digital)
Martin Frigaard not only outlines how to collect, manipulate, summarize, and visualize data, but also explores how to communicate your findings in a convincing way your audience will understand and appreciate. Read more.
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11:00am12:30pm
Location: 210 C/G
Willy Lulciuc (WeWork)
Willy Lulciuc explains how lineage metadata in conjunction with a data catalog helps improve the overall quality of data. You'll dive into complex inter-DAGs dependencies in Airflow and get a hands-on introduction to data lineage using Marquez. You'll also develop strong debugging techniques and learn how to effectively apply them. Read more.
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11:00am12:30pm
Location: 210 D/H
Lukas Biewald (Weights & Biases)
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1:45pm3:15pm
Location: 230 A
Laura Schornack (JPMorgan Chase)
Many pieces go into integrating machine learning models into an application. Laura Schornack details how to create the architecture for each piece so it can be delivered in an agile manner. Along the way, you'll learn how to integrate these pieces into an existing application. Read more.
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1:45pm3:15pm
Location: 210 C/G
Chris Bartholomew (Kafkaesque)
Chris Bartholomew walks you through the architecture and important concepts of Apache Pulsar. You'll set up a local Apache Pulsar environment and use the Python API to do publish/subscribe (pub/sub) message streaming, fanning out messages to multiple consumers. Read more.

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