Corey Brooks

Corey Brooks

1592033216

Intro to Kubeflow Pipelines

Continuous training in production, automatic tracking of metadata, and reusable ML components! These are just some of the ways that Kubeflow Pipelines handle the orchestration of ML workflows. In this episode of Kubeflow 101, Stephanie Wong shows you how Kubeflow Pipelines makes ML workflows easily composable, shareable, and reproducible.

#kubeflow #machine-learning #devops #developer #kubernetes

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Buddha Community

Intro to Kubeflow Pipelines
Martin  Soit

Martin Soit

1600410108

Machine Learning Pipelines with Kubeflow

Why Machine Learning Pipelines?

A lot of attention is being given now to the idea of Machine Learning Pipelines, which are meant to automate and orchestrate the various steps involved in training a machine learning model; however, it’s not always made clear what the benefits are of modeling machine learning workflows as automated pipelines.

When tasked with training a new ML model, most Data Scientists and ML Engineers will probably start by developing some new Python scripts or interactive notebooks that perform the data extraction and preprocessing necessary to construct a clean set of data on which to train the model. Then, they might create several additional scripts or notebooks to try out different types of models or different machine learning frameworks. And finally, they’ll gather and explore metrics to evaluate how each model performed on a test dataset, and then determine which model to deploy to production.

#kubeflow-pipelines #kubernetes #kubeflow #machine-learning

Owen  Lemke

Owen Lemke

1620397920

Kubeflow: Not Yet Ready for Production?

We’re building a **reference machine learning architecture: **a free set of documents and scripts to combine our chosen open source tools into a reusable machine learning architecture that we can apply to most problems.

Kubeflow — a machine learning platform built on Kubernetes, and which has many of the same goals — seemed like a great fit for our project in the beginning. We tried it for several weeks, but after facing several challenges, we’ve now decided to drop it completely.

This article describes our Kubeflow experience. Our goal is to help others see — earlier than we did — that Kubeflow might not be everything it claims to be quite yet.

To be clear: Kubeflow has some shortcomings that prevented us from relying on it for this project. That said, we still respect Kubeflow’s goals, and we hope that as the project matures and addresses some of these issues, we can revisit the idea of using it in the future.

#machine-learning #data-science #kubeflow-pipelines #kubernetes #kubeflow

Corey Brooks

Corey Brooks

1592033216

Intro to Kubeflow Pipelines

Continuous training in production, automatic tracking of metadata, and reusable ML components! These are just some of the ways that Kubeflow Pipelines handle the orchestration of ML workflows. In this episode of Kubeflow 101, Stephanie Wong shows you how Kubeflow Pipelines makes ML workflows easily composable, shareable, and reproducible.

#kubeflow #machine-learning #devops #developer #kubernetes

Anton Palyonko

Anton Palyonko

1621173840

Scaling ML Pipelines with KALE — The Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s

Scaling ML pipelines with KALE — the Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s

One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments. What’s the best setup and what’s involved in getting models being production-ready? Where do you start? In this talk, you will learn about KALE — the Kubeflow Automated Pipeline Engine. With KALE you can finally link the work done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.

#kubeflow #machine-learning

Stracke  Kiana

Stracke Kiana

1598754113

How to Build a Kubeflow Pipeline

Want to learn how to create an ML application from Kubeflow Pipelines? In this episode of Kubeflow 101, we show you how to build a Kubeflow Pipeline from the ML model we explored in the last episode. Moreover, we give you a walkthrough of how to create, test and deploy your ML application in Kubeflow Pipelines. Watch to learn how Kubeflow Pipelines can bring orchestration to complex workflows when working with ML applications.

Last episode → https://goo.gle/2FKcLNF

Timestamps:

0:00 - Intro

0:40 - Pipeline steps overview

1:09 - Import dependencies, define constants

1:45 - Download trainer data

2:10 - Train the model

3:04 - Deploy the model

3:22 - Define and submit the Kubeflow pipeline

3:56 - Compile and share the pipeline

4:26 - Conclusion

Watch more episodes of Kubeflow 101 → https://goo.gle/3cqY2lR

Subscribe to the GCP Channel → https://goo.gle/GCP

#kubeflow #programming