Mar 15–18, 2020

Schedule

Sunday, March 15, 2020

9:00am

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9:00am–5:00pm Sunday, 03/15/2020
Training
Bruno Goncalves (Data For Science, Inc)
Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. In this two day training we will cover a broad range of traditional machine learning and deep learning techniques to model and analyse timeseries datasets with an emphasis on practical applications. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
The aim of the workshop is to provide a thorough introduction to the art and science of building recommendation systems to get a thorough introduction to recommendation systems and paradigms across domains, gain an end-to-end view of deep-learning based recommendation and learning-to-rank systems, understand practical considerations and guidelines for building and deploying recsys. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Delip Rao (AI Foundation)
Delip Rao and Brian McMahan explore natural language processing using a set of machine learning techniques known as deep learning. They walk you through neural network architectures and NLP tasks and teach you how to apply these architectures for those tasks. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Wenming Ye (Amazon Web Services)
Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye, Miro Enev, and Mahendra Bairag detail a practical next step in DL learning with instructions, demos, and hands-on labs. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Jesse Anderson (Big Data Institute)
Jesse Anderson leads a deep dive into Apache Kafka. You'll learn how Kafka works and how to create real-time systems with it. You'll also discover how to create consumers and publishers in Kafka and how to use Kafka Streams, Kafka Connect, and KSQL as you explore the Kafka ecosystem. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
David Anderson (Ververica)
A hands-on introduction to Apache Flink for Java and Scala developers who want to learn to build streaming applications. The curriculum will focus on the core concepts of distributed streaming dataflows, event time, and key-partitioned state, while also looking in depth at the runtime, ecosystem, and use cases. The exercises help you understand how the pieces fit together to solve real problems. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Rich Ott (The Pragmatic Institute), Michael Li (The Data Incubator)
You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Robert Schroll (The Pragmatic Institute)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00am–5:00pm Sunday, 03/15/2020
Training
Don Fox (Pragmatic Institute)
We will walk through all the steps - from prototyping to production - of developing a machine learning pipeline. We’ll look at data cleaning, feature engineering, model building/evaluation, and deployment. Students will extend these models into two applications from real-world datasets. All work will be done in Python. Read more.
9:00am–5:00pm Sunday, 03/15/2020
TBC
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9:00am–5:00pm Sunday, 03/15/2020
Training
Organizations using a single platform for processing all types of big data workloads are able to manage growth and complexity, react faster to customer needs, and improve collaboration–all at the same time. In this hands-on workshop, you will leverage Apache Spark and Hive to build an end-to-end solution to address business challenges common in retail and eCommerce. Read more.

Monday, March 16, 2020

9:00am

9:00am–5:00pm Monday, 03/16/2020
TBC
9:00am–5:00pm Monday, 03/16/2020
TBC
9:00am–5:00pm Monday, 03/16/2020
TBC
9:00am–5:00pm Monday, 03/16/2020
TBC
9:00am–5:00pm Monday, 03/16/2020
TBC
9:00am–5:00pm Monday, 03/16/2020
TBC
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
AI strategy
Jike Chong (LinkedIn), Yue Cathy Chang (TutumGene)
More than 85% of data science projects fail. This high failure rate is a main reason why data science is still a “science”. As data science practitioners, how can we reduce this failure rate? In this tutorial, we first focus on the three key steps of applying data science technology to business problems, then discuss three areas of concerns for applying domain insights in AI/ML initiatives. Read more.
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9:00am–12:30pm Monday, 03/16/2020
Paroma Varma (Snorkel AI)
This tutorial will walk participants through building and managing training datasets programmatically with Snorkel (snorkel.org), an open source framework developed at the Stanford AI lab, and show how this can lead to more efficiently building and managing machine learning (ML) models in a range of practical settings. Read more.
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
Robert Crowe (Google)
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe outlines what's involved in creating a production ML pipeline and walks you through working code. Read more.
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9:00am–12:30pm Monday, 03/16/2020
Catherine Nelson (Concur Labs / SAP Concur), Hannes Hapke (Wunderbar.ai)
Most deep learning models don’t get analyzed, validated and deployed. In this tutorial, we’ll explain the necessary steps to release machine learning models for real world applications. We’ll take the audience through an example project using the TensorFlow ecosystem, focussing on how to analyze models and how to deploy them efficiently. Read more.
9:00am–12:30pm Monday, 03/16/2020
TBC
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9:00am–12:30pm Monday, 03/16/2020
Fatma Tarlaci (Quansight)
Language is at the heart of everything we, humans, do. Natural Language Processing is one of the most challenging tasks of Artificial Intelligence, mainly due to the difficulty of detecting nuances and commonsense reasoning in natural language. This tutorial invites those who are interested in learning more about NLP and getting a complete hands-on implementation of an NLP deep learning model. Read more.
9:00am–12:30pm Monday, 03/16/2020
TBC
9:00am–12:30pm Monday, 03/16/2020
TBC
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
Alice Zhao (Metis)
As a data scientist, we are known to crunch numbers, but what happens when we run into text data? In this tutorial, I will walk through the steps to turn text data into a format that a machine can understand, share some of the most popular text analytics techniques, and showcase several natural language processing (NLP) libraries in Python including NLTK, TextBlob, spaCy and gensim. Read more.
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
David Anderson (Ververica), Seth Wiesman (Ververica)
This tutorial demonstrates that building and managing scalable, stateful, event driven applications can be easier and more straightforward than you might expect. We’ll provide a hands-on introduction to this topic as we implement a ridesharing application together. Read more.
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
Sourav Dey (Manifold), Alex Ng (Manifold)
Today, ML engineers are working at the intersection of data science and software engineering—that is, MLOps. This tutorial walks through the six steps of the Lean AI process and explains how it helps ML engineers work as an an integrated part of development and production teams. We’ll also work through a hands-on example using real-world data, so you can get up and running seamlessly. Read more.
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
Danilo Sato (ThoughtWorks)
We will walk you through applying continuous delivery (CD), pioneered by ThoughtWorks, to data science and machine learning. Join in to learn how to make changes to your models while safely integrating and deploying them into production, using testing and automation techniques to release reliably at any time and with a high frequency. Read more.
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9:00am–12:30pm Monday, 03/16/2020
3-hour tutorial
Sarah Bird (Microsoft), Mehrnoosh Sameki (MERS) (Microsoft)
Main focus: Six core principles of responsible AI: fairness, reliability/safety, privacy/security, inclusiveness, transparency and accountability. We will focus on Transparency (Interpretability), Fairness, and Privacy and cover best practices and state-of-the-art open source toolkits that empower researchers, data scientists, and stakeholders to build more trustworthy AI systems. Read more.

1:30pm

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1:30pm–5:00pm Monday, 03/16/2020
Ira Cohen (Anodot)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores the cycle of developing ML-based capabilities (or entire products) and the role of the (product) manager in each step of the cycle. Read more.
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1:30pm–5:00pm Monday, 03/16/2020
Mars Geldard (University of Tasmania), Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee)
Mars Geldard, Tim Nugent, and Paris Buttfield-Addison say you're wrong if you think Swift is just for app developers. Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries) and Swift—the safe, fast, incredibly capable open source programming language; Swift for TensorFlow is the perfect way to learn deep learning and Swift. Read more.
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
Ethics and AI
Dennis Wei (IBM Research)
Learn to use and contribute to the new open-source Python package AI Explainability 360 directly from its creators. Architected to translate new developments from research labs to data science practitioners in industry, this is the first comprehensive toolkit for explainable AI, including eight diverse and state-of-the-art methods from IBM Research. Read more.
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1:30pm–5:00pm Monday, 03/16/2020
Pramod Singh (Publicis Sapient), Akshay kulkarni (Publicis Sapient)
Pramod Singh and Akshay Kulkarni walk you through the in-depth process of building a text summarization model through attention network using TensorFlow 2.0 You'll gain the practical hands-on knowledge to build and deploy a scalable text summarization model on top of Kubeflow. Read more.
1:30pm–5:00pm Monday, 03/16/2020
TBC
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn. Read more.
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
Robert Horton (Microsoft), Mario Inchiosa (Microsoft), John-Mark Agosta (Microsoft)
This workshop introduces the fundamental concepts of ML to business and healthcare decision makers and software product managers so that they will be able to make more effective use of machine learning results, and be better able to evaluate opportunities to apply ML in their industries. The optional exercises require a web browser and Microsoft Excel. Read more.
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
Robert Nishihara (University of California, Berkeley), Ion Stoica (University of California, Berkeley), Philipp Moritz (University of California, Berkeley)
Surprisingly, there is no simple way to scale up Python applications from your laptop to the cloud. Ray is an open source framework for parallel and distributed computing that makes it easy to program and analyze data at any scale by providing general-purpose high-performance primitives. This tutorial will show how to use Ray to scale up Python applications, data processing, and machine learning. Read more.
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
David Talby (Pacific AI), Alex Thomas (John Snow Labs), Claudiu Branzan (Accenture)
This is a hands-on tutorial on applying the latest advances in deep learning for common NLP tasks such as named entity recognition, document classification, sentiment analysis, spell checking and OCR. Learn to build complete text analysis pipelines using the highly performant, high scalable, open-source Spark NLP library in Python. Read more.
1:30pm–5:00pm Monday, 03/16/2020
TBC
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Machine learning models are data, which means they require the same data governance considerations as the rest of your data. In this tutorial we will concentrate on metadata management for model serving. We will discuss what information about running systems we need and why it is important. We will also show how Apache Atlas can be used for storing and managing this information. Read more.
1:30pm–5:00pm Monday, 03/16/2020
TBC
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1:30pm–5:00pm Monday, 03/16/2020
3-hour tutorial
Patrick Hall (H2O.ai | George Washington University)
Even if you've followed current best practices for model training and assessment, machine learning models can be hacked, socially discriminatory, or just plain wrong. This presentation introduces model debugging strategies to test and fix security vulnerabilities, unwanted social biases, and latent inaccuracies in models. Read more.

Tuesday, March 17, 2020

8:00am

8:00am–8:30am Tuesday, 03/17/2020
Event
TBC

12:30pm

12:30pm–1:45pm Tuesday, 03/17/2020
Event
Lunch TBC
12:30pm–1:45pm Tuesday, 03/17/2020
Event
TBC

Wednesday, March 18, 2020

8:00am

8:00am–8:30am Wednesday, 03/18/2020
Event
TBC

12:30pm

12:30pm–1:45pm Wednesday, 03/18/2020
Event
TBC

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