O'Reilly Strata Data & AI Conference
This is where cutting-edge science and new business fundamentals intersect. It's a deep dive into emerging data and ML techniques and technologies.
View eventEverything 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.
View eventThe 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.
View eventBig 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.
View eventSCaLE is the largest community-run open-source and free software conference in North America.
View eventO'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.
View eventThe 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.
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