The Next Battleground for Deep Learning Performance
The frameworks are in place, the hardware infrastructure is robust, but what has been keeping machine learning performance at bay has far less to do with the system-level capabilities and more to do with intense model optimization.
On the Radar: SigOpt for machine learning algorithm optimization
The potential for machine learning systems, such as those based on deep learning, depends on organizations having the skill to develop their models and fine tune the multitude of model configuration parameters (known as hyperparameters).
The Latest In ML Ops – 5 Evolutions of Production ML
Tomorrow, 9/22 at 12pm PT: Join us and @MLconf for an applied #ML webinar walking through workflows for fraud detection and image classification. Follow along in your notebook with a dataset, architecture and code examples that we will provide: https://t.co/v9Tt3hHs0Y https://t.co/XQBIHBAaiH
Don't forget to catch @_notorious_meg's #BERT talk at #RaySummit on September 30th! Catch Meghana and the other great speakers at this free event. Learn more here: https://t.co/PQZDBNrXHz
With @MLconf next week, join us for a live walkthrough of how to use Experiment Management to manage your workflows for image classification and fraud detection, followed by a Q&A with SigOpt's @jimblomo: https://t.co/AZbNjA6PrD