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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.

David Anderson and Seth Wiesman lead a hands-on introduction to Apache Flink for Java and Scala developers who want to learn to build streaming applications. You'll focus on the core concepts of distributed streaming data flows, event time, and key-partitioned state, while looking at runtime, ecosystem, and use cases with exercises to help you understand how the pieces fit together.

The instructors 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.

The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources. This architecture is ideal for implementing neural networks. You'll be introduced to TensorFlow's capabilities in Python, moving from building machine learning algorithms piece-by-piece to using the Keras API provided by TensorFlow with several hands-on applications.

You'll walk through all the steps—from prototyping to production—of developing a machine learning pipeline. After looking at data cleaning, feature engineering, model building and evaluation, and deployment, you'll extend these models into two applications from real-world datasets. All your work will be done in Python.

Jorge Villamariona outlines how 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. You'll leverage Apache Spark and Hive to build an end-to-end solution to address business challenges common in retail and ecommerce.

The TensorFlow library provides computational graphs with automatic parallelization across resources—ideal architecture for implementing neural networks. You'll walk through TensorFlow's capabilities in Python, from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow, with several hands-on applications.

Bargava Subramanian and Amit Kapoor provide you with a thorough introduction to the art and science of building recommendation systems and paradigms across domains. You'll get an end-to-end overview of deep learning-based recommendation and learning-to-rank systems to understand practical considerations and guidelines for building and deploying RecSys.

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. Join in to get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott.

Delip Rao explores natural language processing (NLP) using a set of machine learning techniques known as deep learning. He walks you through neural network architectures and NLP tasks and teaches you how to apply these architectures for those tasks.

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 demonstrates a practical next step in DL learning with instructions, demos, and hands-on labs.

Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Join Nikki Rouda 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.

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. Bruno Goncalves explains a broad range of traditional machine learning (ML) and deep learning techniques to model and analyze time series datasets with an emphasis on practical applications.

Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine San Jose has to offer.