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As teams look to productionize their ML efforts, versioning, tagging, and labeling the data becomes even more difficult. The challenge for MLOps and DataOps teams will be to operationalize their data to better meet the needs of their end-users.
View eventWe’re excited to announce the release of Hub 2.0! With version 2.0 we’ve added features that make it even easier for your ML teams to get models to production, while also saving you money on processing and storage costs!
View eventFor 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 GitHubIn this example we'll create a machine learning pipeline that generates tweets using OpenAI's gpt-2 text generation model.
View on GitHubThis example demonstrates how you can evaluate a model or function in a distributed manner on multiple sets of parameters.
View on GitHubThis 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 GitHubThis example uses the canonical mnist dataset, Kubeflow, TFJobs, and Pachyderm to demonstrate an end-to-end machine learning workflow with data provenance.
View on GitHubIn 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
