Story Tag: kubeflow

Stories Tag: kubeflow

Kubeflow: Not Yet Ready for Production? | by Markus Schmitt | Jan, 2021 | Towards Data Science

admin 3 weeks ago in

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…

A Machine Learning Workflow on the Deployment and Prediction of Unknown Logs using Kubeflow Pipeline, Bagging and Boosting Methods. |...

admin 4 weeks ago in

Recent and rapid advancements in machine learning has given rise to many applications of ML techniques to real world problems. The oil and gas industry, with its vast amount of subsurface data, is well positioned to leveraging these approaches to deliver significant benefit. This subsurface data consists of measurements of physical properties gathered while drilling boreholes and are recorded in well logs, which are sequential…

Kubeflow Tutorial-Simplified ML Model Serving | by Josh Bottum | Nov, 2020 | Medium

admin 2 months ago in

Serving an ML model can be complicated. This new Kubeflow tutorial (from KubeCon) shows how data scientists can deploy their models (from a Jupyter Notebook) with a simplified Kale-based workflow. According to this Gartner blog post, the majority of ML projects will have challenges i.e. “Through 2022, only 20% of analytic insights will deliver business outcomes”. As I map this to Kubeflow workflows, I have…

Distributed Hyperparameter Search in Kubeflow/Kubernetes: Keras Tuner vs. Katib | by Roman Kazinnik | Nov, 2020 | Medium

admin 2 months ago in

Although Katib is a Kubeflow built-in Hyperparameter Search (HS), here is why I choose Keras-Tuner for distributed HS: HS’s goal is to produce a list of model architectures and model parameters sorted by its model accuracy score. Asynchronous HS will produce such a list progressively by having it updated with newly computed trials. Keras Tuner allows making HS distributed with a set of CPU or…

Quick Start with Kubeflow Pipelines on Google Cloud Platform | by Saiteja Jayanthi | Nov, 2020 | Medium

admin 2 months ago in

The main purpose of this article is to demonstrate the way to install and use Kubeflow on Google Cloud Platform(GCP). There are multiple sections here that narrate a typical Machine Learning(ML) life cycle, the introduction to Kubeflow, the procedure to set up, creating sample pipelines, and finally, ends with use case scenarios. When most people hear of machine learning, they often jump first to building…

Machine Learning as a Flow: Kubeflow vs. Metaflow | by Roman Kazinnik | Nov, 2020 | Medium

admin 3 months ago in

Both Kubeflow (2018, Google) and Metaflow (2019, Netflix) are great Machine Learning platforms for experimentation, development, and production deployment. Having used both of these, here is my comparative analysis. For starters, Kubeflow is a project that helps you deploy machine learning workflows on Kubernetes. On the other hand, Metaflow is a Python library that helps data scientists build and manage real-life data science projects. Both…

Installing Kubeflow on macOS with MicroK8s | by Dan Siwiec | Dan On Coding | Oct, 2020 | Medium

admin 3 months ago in

Simple steps for getting Kubeflow off the ground on your Mac (hint: it’s not ‘helm install…’) MicroK8s is a fully-compliant mini Kubernetes implementation. Thanks to its tiny footprint, it is perfectly suited for local installation. We’ll install it with brew: Once it’s installed on your machine, we can provision a local cluster this way: The additional –cpu and –mem parameters increase the CPU count and…