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.
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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.