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Spark + AI Summit North America 2019

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Event Information
Event Date: April 23, 2019 - April 25, 2019
Event Location: San Francisco, CA, United States
Event Phone: 1-866-330-0121

Welcome to the largest data & machine learning conference in the world. It's a unique experience for developers, data engineers, data scientists, and decision-makers to collaborate at the intersection of data and ML. Attendees will learn about the latest advances in Apache Spark and ML technologies like TensorFlow, MLflow, PyTorch as well as real-world enterprise AI best practices.

Data and AI need to be unified: the best AI applications require massive amounts of constantly updated training data to build state-of-the-art models. So far. Apache Spark is the only unified analytics engine that combines large-scale data processing with state-of-the-art machine learning and AI algorithms

The sessions and training at this conference will cover data engineering and data science content, along with best practices for productionizing AI: keeping training data fresh with stream processing, quality monitoring, testing, and serving models at massive scale. The conference will also include deep-dive sessions on popular software frameworks—e.g., TensorFlow, SciKit-Learn, Keras, PyTorch, DeepLearning4J, BigDL, and deep learning pipelines.

Combining Spark + AI topics, this conference is a unique “one-stop shop” for developers, data scientists, and tech executives seeking to apply the best tools in data and AI to build innovative products. Join more than 5,000 engineers, data scientists, AI experts, researchers, and business professionals for three days of in-depth learning and networking.


  • What’s coming next in Apache Spark
  • Best Practices for Productionizing Machine Learning
  • Managing your Machine Learning Lifecycle with MLflow
  • Latest Deep Learning and Machine Learning Frameworks
  • Unified Analytics Platforms to combine Data and AI
  • Practical, real-world Artificial Intelligence Use Cases
  • Using Apache Spark at scale in a variety of applications
  • Structured Streaming and Continuous Applications


  • AI
  • Data Science
  • Deep Learning Techniques
  • Productionizing ML
  • Developer
  • Enterprise
  • Python & Advanced Analytics
  • Research
  • Technical Deep Dives
  • Apache Spark Use Cases & Ecosystem