Put AI to Work
April 15-18, 2019
New York, NY

Risk-free deep learning without sacrificing performance

Evan Sparks (Determined AI)
1:00pm1:40pm Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
Secondary topics:  Deep Learning and Machine Learning tools, Platforms and infrastructure
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists, machine learning engineers, and directors of machine learning



Prerequisite knowledge

  • A basic understanding of deep learning (e.g., how does training happen, how are models initialized, how do they get updated over time, and how much time and effort goes into training a deep learning model?)
  • Experience with common application frameworks like TensorFlow, Keras, and PyTorch and source controls systems like Git (useful but not required)

What you'll learn

  • Learn what inputs are necessary for reproducibility in your model development workflow and how to adapt your processes to track these inputs and incorporate them in your development process
  • Understand why insisting on reproducibility can slow down computational performance
  • Discover how to maintain high performance while retaining reproducibility


Building applications powered by deep learning is hard—particularly in an enterprise setting when reproducibility is of paramount importance. It’s difficult to justify shipping a model to production when you can’t be sure you’ll be able to build it again. Achieving reproducibility during deep learning model development comes at a cost: it can be difficult to maintain a high degree of computational performance during the model development lifecycle while retaining the benefits of reproducibility.

Evan Sparks describes the key ingredients of reproducible deep learning models in an enterprise setting. He then explains how to maintain a high degree of resource utilization and throughput through workload-aware cluster resource orchestration techniques.

Photo of Evan Sparks

Evan Sparks

Determined AI

Evan Sparks is cofounder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from UC Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.

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