Today we’re excited to announce the public beta of our fully-managed and hosted offering of Pachyderm: Hub. When my co-founder Joey Zwicker and I started Pachyderm nearly 6 years ago, we set out to make data science better. Pachyderm’s current feature set provides collaboration on large scale data workloads, but it’s confined to companies with the resources to manage a Kubernetes cluster. We’ve felt for a long time, that the last part has been holding us back.Read full article
What it's like to build kubernetes-as-a-service, as a service.Read Full Article
Pachyderm 1.9 is out nowRead Full Article
See how Digital Reasoning is preparing for the future with Pachyderm.Read Full Article
New Pachyderm Example that uses a RNN to genereate new Game Of Thrones scripts.Read Full Article
4 Reasons to Get Excited About Pachyderm 2019Read Full Article
Everything from webinars to conference announcements and more.
For those who like to learn while doing.
This tutorial walks you through the deployment of a Pachyderm pipeline to do simple edge detection on a few images.View on GitHub
In this example we'll create a machine learning pipeline that generates tweets using OpenAI's gpt-2 text generation model.View on GitHub
This example demonstrates how you can evaluate a model or function in a distributed manner on multiple sets of parameters.View on GitHub
This example connects to an IMAP mail account, collects all the incoming mail and analyzes it for positive or negative sentiment, sorting the emails into directories in its output repo with scoring information added to the email header "X-Sentiment-Rating"View on GitHub
This example uses the canonical mnist dataset, Kubeflow, TFJobs, and Pachyderm to demonstrate an end-to-end machine learning workflow with data provenance.View on GitHub
In this example, we will create a join pipeline. A join pipeline executes your code on files that match a specific naming pattern.View on GitHub