Presented By O’Reilly and Intel AI
Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Software development in the age of deep learning

Evan Sparks (Determined AI), Ameet Talwalkar (Carnegie Mellon University | Determined AI)
1:45pm-2:25pm Thursday, September 6, 2018
Implementing AI, Models and Methods
Location: Imperial A
Secondary topics:  Deep Learning models, Edge computing and Hardware, Platforms and infrastructure
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Machine learning engineers, systems engineers, and data scientists

Prerequisite knowledge

  • A basic understanding of machine learning and deep learning
  • Ideas for applications you'd like to build or have built using ML and DL techniques (useful but not required)
  • Familiarity with existing deep learning application frameworks, such as TensorFlow, Keras, or Caffe (useful but not required)

What you'll learn

  • Understand the conventional workflow associated with building a deep learning model and shipping it in production
  • Learn the key deficiencies of this workflow and how to address them through better resource management, careful use of multi-GPU and multimachine parallelism, automatic hyperparameter optimization, and an integrated approach to training and deployment


In spite of the enormous excitement about the potential of deep learning, building practical applications powered by deep learning remains an enormous challenge: the necessary expertise is scarce, the hardware requirements can be prohibitive, and current software tools are immature and limited in scope.

Drawing on academic work done at CMU, Berkeley, and UCLA, as well as their experience at Determined AI, a startup that provides software to make deep learning engineers dramatically more productive, Evan Sparks and Ameet Talwalkar outline the key factors that distinguish data-driven application development from classical logic-driven software engineering and their implications in the context of statistical accuracy, computational performance, debugging, and reproducibility. They then describe several promising opportunities to drastically improve data-driven application development via novel algorithmic and software solutions, including automated hyperparameter optimization, efficient utilization of distributed resources via performance models, and reproducible workflow management.

Photo of Evan Sparks

Evan Sparks

Determined AI

Evan Sparks is a 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 the University of California, 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.

Photo of Ameet Talwalkar

Ameet Talwalkar

Carnegie Mellon University | Determined AI

Ameet Talwalkar is cofounder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. Ameet led the initial development of the MLlib project in Apache Spark. He is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press) and teaches an award-winning MOOC on edX, Distributed Machine Learning with Apache Spark.