Posts in: Blog

SEP 10, 2020

Lightning-fast ML pipelines with Determined and Kubeflow

By David Hershey

Learn how to do production-grade MLOps with scalable, automated machine learning training and deployment using Determined, Kubeflow Pipelines, and Seldon Core.

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SEP 03, 2020

Faster NLP with Deep Learning: Distributed Training

By Dave Troiano

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.

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AUG 11, 2020

End-to-End Deep Learning with Spark, Determined, and Delta Lake

By David Hershey

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.

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AUG 05, 2020

YogaDL: a better approach to data loading for deep learning models

By Aaron Harlap

A better approach to loading data for deep learning models.

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JUL 30, 2020

TensorFlow Datasets: The Bad Parts

By Katie Porterfield, Yoav Zimmerman

We peek behind the curtain of TensorFlow Datasets to reveal some surprising problems.

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JUL 30, 2020

Choosing Your Deep Learning Infrastructure: The Cloud vs. On-Prem Debate

By Jennifer Villa, Dave Troiano

We compare cloud and on-prem deep learning infrastructure options across five key criteria.

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JUL 17, 2020

In defense of weight-sharing for neural architecture search: an optimization perspective

By Misha Khodak, Liam Li

A geometry-aware approach to optimization for neural architecture search.

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JUN 24, 2020

How to Build an Enterprise Deep Learning Platform, Part Three

By David Hershey

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.

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JUN 12, 2020

How to Structure your Code to Build a Scalable Deep Learning Model

By David Hershey

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.

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MAY 27, 2020

How to Build an Enterprise Deep Learning Platform, Part Two

By David Hershey

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.

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