Everything from webinars to conference announcements and more.
AI/Machine learning is a hot field topic in engineering and tech right now - it feels like every organization is trying to realize the benefits of how they can get their data to work for them. The reality is that it’s hard to do, especially at a meaningful scale that will truly impact the business.
View eventJust a few years ago every cutting-edge tech company, like Google, Lyft, Microsoft, and Amazon, rolled their own AI/ML tech stack from scratch. Fast forward to today and we have a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. But how do we make sense of it all? In order for AI apps to become as ubiquitous as the apps on your phone, you need a canonical stack for machine learning that makes it easier for non-tech companies to level up fast.
View eventIn this webinar, Vincent Koops, Senior Data Scientist at RTL Netherlands, will walk through their Video AI platform at RTL and how they’ve addressed these challenges. Their platform is built on top of Pachyderm and Kubernetes to enable a wide range of ML applications such as automatic thumbnail picking and mid-roll marking.
View eventAs 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
