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.
A geometry-aware approach to optimization for neural architecture search.
See an enterprise deep learning platform in action that comprises Pachyderm for data management, Determined for model development and training, and Seldon Core for deployment.
How you structure your machine learning codebase has a big impact on how easy it is to scale, including adding support for distributed training, hyperparameter tuning, and experiment tracking.
How to build a deep learning platform with open source components to handle tasks such as training data management and versioning, scalable model training, and deployment.
There is an incredibly common story playing out in countless enterprises in 2020.
With Determined you can cloud burst your deep learning training workloads on GCP’s cost-effective preemptible GPUs, in a way that is friendly to infrastructure teams and model developers alike.