Learn how Determined's built-in model registry makes it simple to move deep learning models from research to production deployments.
Takeaways from our experience building state-of-the-art hyperparameter tuning in Determined AI’s integrated deep learning training platform.
Join us here for our podcast series! These conversations will cover a wide range of areas in computer science and machine learning with some of the brightest minds in academia and industry. Our first episode is with 2017 Turing Award winner Dave Patterson.
The Determined deep learning training platform now runs natively on Kubernetes, providing a simpler way to manage on-prem and cloud GPU resources.
Learn how to do production-grade MLOps with scalable, automated machine learning training and deployment using Determined, Kubeflow Pipelines, and Seldon Core.
Training deep learning models for NLP tasks typically requires many hours or days to complete on a single GPU. In this post, we leverage Determined’s distributed training capability to reduce BERT for SQuAD model training time from hours to minutes, without sacrificing model accuracy.
How to build an end-to-end deep learning pipeline, including data preprocessing with Spark, versioned data storage with Delta Lake, distributed training with Determined, and batch inference with Spark.
A better approach to loading data for deep learning models.
We peek behind the curtain of TensorFlow Datasets to reveal some surprising problems.
We compare cloud and on-prem deep learning infrastructure options across five key criteria.