Published Dec 4, 2017
Scaleable Distributed Deep Learning with Hillery Hunter - #77
IBM Fellow Hillery Hunter delves into scaling challenges and innovations for neural networks, emphasizing the transformative power of deep learning in enterprise applications like risk and fraud forecasting. The episode highlights the Distributed Deep Learning library's unique framework independence and synchronous training capabilities to boost AI scalability and performance.

Topics covered
Popular Clips
Episode Highlights
Related Episodes


Building AI Products with Hilary Mason - #11
Answers 383 questions

Scaling TensorFlow at LinkedIn with Jonathan Hung - #314
Answers 383 questions

Agile Data Science with Sarah Aerni - #143
Answers 383 questions

Deep Learning, Transformers, and the Consequences of Scale with Oriol Vinyals - #546
Answers 383 questions

Full-Stack AI Systems Development with Murali Akula - #563
Answers 383 questions

Systems and Software for Machine Learning at Scale with Jeff Dean - #124
Answers 383 questions

Scaling Enterprise ML in 2020: Still Hard! with Sushil Thomas - #429
Answers 383 questions
Deep Gradient Compression for Distributed Training with Song Han - #146
Answers 383 questions

Bighead: Airbnb's Machine Learning Platform with Atul Kale - TWiML Talk #198
Answers 383 questions

Productive Machine Learning at LinkedIn with Bee-Chung Chen - TWiML Talk #200
Answers 383 questions

Scaling Deep Learning: Systems Challenges & More with Shubho Sengupta - #14
Answers 383 questions

Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306
Answers 383 questions














