Sep 23–26, 2019

2-Day Training Courses

All training courses take place 9:00am–5:00pm, Monday, September 23–Tuesday, September 24 and are limited in size to maintain a high level of hands-on learning and instructor interaction.

Participants should plan to attend both days of training course. Note: to attend training courses, you must be registered for a Platinum or Training pass; does not include access to tutorials on Tuesday.

Learn more about a free 1-day training course, Machine Learning for the Enterprise (sponsored by IBM), available to Bronze pass holders.

Monday, September 23 - Tuesday, September 24

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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 01/02
Michael Li (The Data Incubator), Gonzalo Diaz (The Data Incubator)
Michael Li and Gonzalo Diaz provide a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1E 07
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil explores TensorFlow's capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow's Keras API with several hands-on applications. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 15/16
Michael Cullan (The Data Incubator)
Michael Cullan walks you through developing a machine learning pipeline from prototyping to production. You'll learn about data cleaning, feature engineering, model building and evaluation, and deployment and then extend these models into two applications from real-world datasets. All work will be done in Python. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1E 06
Jesse Anderson (Big Data Institute)
Jesse Anderson offers you an in-depth look at Apache Kafka. You'll learn how Kafka works and how to create real-time systems with it, as well as how to create consumers and publishers. You'll take a look Jesse then walks you through Kafka’s ecosystem, demonstrating how to use tools like Kafka Streams, Kafka Connect, and KSQL. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 03
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 17
Jorge Lopez (Amazon Web Services), Radhika Ravirala (Amazon Web Services), Nikki Rouda (Amazon Web Services), Jesse Gebhardt (Amazon Web Services), Rajeev Chakrabarti (Amazon Web Services)
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Join the AWS team to learn how to incorporate serverless concepts into your big data architectures. You'll explore design patterns to ingest, store, and analyze your data as you build a big data application using AWS technologies such as S3, Athena, Kinesis, and more. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 18
Ian Cook (Cloudera)
Advancing your career in data science requires learning new languages and frameworks—but you face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by outlining the abstractions common to these systems. You'll go hands-on exercises to overcome obstacles to getting started using new tools. Read more.

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