We spent 2019 on the road, going to conference after conference and talking to thousands of data scientists, developers, and MLOps engineers. But more than anything, we listened. We wanted to know the struggles and challenges that data science teams face every day. After listening to various use-cases and learning about their struggles to make things work, we continue to see that data science is not owned by individual contributors; it’s a collective responsibility. Pachyderm was always a platform focused deeply on collaboration but with version 1.10 we looked to build on that rock-solid foundation and make collaboration a way of life from end to end.Read full article
How LogMeIn uses Pachyderm to decrease training time by 99%Read More
Epona uses Pachyderm to streamline their entire machine learning genetics pipeline while also reducing prediction times from days to just minutes.Read More
Digital Reasoning exploring the future of Machine Learning with PachydermRead More
Pachyderm customer case study: AgBiome uses Pachyderm to streamline genomic data science.Read More
General Fusion relies on Pachyderm for its data versioning capabilitesRead More
AutoML promises to make AI easy for non data scientists but is that a reality in 2020 or just a dream?Read Full Article
Pachyderm 1.10 makes it super easy to connect with JupyterHub. Seamless Single Sign On and a smoothly scripted way to deploy Hub and Pachy together will have your data science team more productive than ever.Read Full Article
Pachyderm 1.10 delivers Kubeflow support you can count on. Leverage Pachyderm’s powerful data lineage platform with TFJobs (or any other Kubeflow run) directly within the Kubeflow ecosystem.Read Full Article
Data science is hard enough without having to pick up a complicated new command line. With the 1.10 release, we give you Pachyderm Shell, delivering time-saving auto-completion, combined with helpful suggestions displayed right in the prompt.Read Full Article
Watch our talk on how to get rid of bias using Pachyderm at the SCALE18x conference.Read Full Article
Everything from webinars to conference announcements and more.
This is where cutting-edge science and new business fundamentals intersect. It's a deep dive into emerging data and ML techniques and technologies.
The largest Predictive Analytics World event to date – Mega-PAW – where the year’s only PAW Business will be held alongside PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World.
Big Data and AI Toronto is co-located with Cybersecurity Toronto and Cloud Toronto to provide you with a unique 4-in-1 learning experience that is engineered to meet your data needs and challenges.
SCaLE is the largest community-run open-source and free software conference in North America.
O'Reilly TensorFlow World brings together the vibrant and growing ecosystem that's driving today’s powerful neural networks—and impacting everything from healthcare to finance, the IoT, and beyond.
The DDDP 2020 agenda stands to tackle the core challenges E&P companies face in leveraging digital solutions to boost production, reduce downtime and increase the bottom-line.
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.
In this example we'll create a machine learning pipeline that generates tweets using OpenAI's gpt-2 text generation model.
This example demonstrates how you can evaluate a model or function in a distributed manner on multiple sets of parameters.
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"
This example uses the canonical mnist dataset, Kubeflow, TFJobs, and Pachyderm to demonstrate an end-to-end machine learning workflow with data provenance.
In this example, we will create a join pipeline. A join pipeline executes your code on files that match a specific naming pattern.