• OCT 29, 2020

    Alex Ratner on Programmatic Data Labeling for Machine Learning

    We chat with Alex Ratner, CEO of Snorkel AI, on the importance of programmatic data labeling in a machine learning workflow.

  • OCT 15, 2020

    Joe Hellerstein on Data Wrangling and Reproducibility

    Our podcast conversation with Joe Hellerstein, co-founder of Trifacta, on the unique challenges of data preparation and reproducibility.

  • OCT 14, 2020

    Announcing Determined 0.13.6

    We are excited to announce the 0.13.6 release of the Determined deep learning training platform.

  • OCT 13, 2020

    Using Determined’s Model Registry To Simplify Model Deployment

    Learn how Determined's built-in model registry makes it simple to move deep learning models from research to production deployments.

  • OCT 08, 2020

    Why Does No One Use Advanced Hyperparameter Tuning?

    Takeaways from our experience building state-of-the-art hyperparameter tuning in Determined AI’s integrated deep learning training platform.

  • SEP 30, 2020

    A Conversation With Dave Patterson

    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.

  • SEP 24, 2020

    Determined Now Supports Kubernetes!

    The Determined deep learning training platform now runs natively on Kubernetes, providing a simpler way to manage on-prem and cloud GPU resources.

  • SEP 10, 2020

    Lightning-fast ML pipelines with Determined and Kubeflow

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

  • SEP 03, 2020

    Faster NLP with Deep Learning: Distributed Training

    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.

  • AUG 11, 2020

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

    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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