Rui Vasconcelos

TL;DR: How you deploy models into production is what separates an academic exercise from an investment in ML that is value-generating for your business. At scale, this becomes painfully complex. This guide walks you through industry best practices and methods, concluding with a practical tool, KFServing, that tackles model serving at scal

TL;DR: KFServing is a novel cloud-native multi-framework model serving tool for serverless inference. A bit of history KFServing was born as part of the Kubeflow project, a joint effort between AI/ML industry leaders to standardize machine learning operations on top of Kubernetes. It aims at solving the difficulties of model deployment to

MLOps community jewels The MLOps community continues to grow and gift us with great content and discussions around the topic! Here are a couple of interesting discussions – a long one (1h) about Kubeflow, feature stores, and other platforms in the MLOps space, and a short one (3 min) on how to manage dependencies: Sneak

In June 2020, AWS introduced SageMaker components for Kubeflow. 6 months later, Antje Barth, Sr. developer advocate @AWS, presents how to build end-to-end ML workflows with Kubeflow Pipelines and how to leverage the benefits of Kubeflow Pipelines and SageMaker altogether. AWS re:invents end-to-end ML workflows Watch the video below: If yo

From Gitlab to Kubeflow in Healthcare ML Lifen, the french platform for healthcare products, recently switched from Gitlab’s jobs to Kubeflow Pipelines for continuous learning capabilities and showcases the transition and its benefits. Check out the blog post Kubeflow for AI in the Telco industry Maciej Mazur, Product Manager @Canonical f

Scaling Keras on Kubernetes with Kubeflow In this week’s blog post, Kirill Goltsman has deconstructed how to use Kubeflow, TFOperator, MPI Operator to train and deploy Keras models at scale. Check out his blog post! How Kubeflow & MLOps can help security David Aronchick, co-founder of Kubeflow and head of OSS ML Strategy at Microsoft

Canonical, the publisher of Ubuntu, announces Kubeflow operators and packages. Within the last week, Canonical announced two new technologies that aim at improving the Kubeflow experience: Charmed Kubeflow – A set of Kubeflow charm operators, that leverage Juju OLM technology for lifecycle management of the applications inside Kubeflow. R

Kubeflow, the ML toolkit on K8s, now fits on your desktop and edge devices! 🚀 Data science workflows on Kubernetes Kubeflow provides the cloud-native interface between Kubernetes and data science tools: libraries, frameworks, pipelines, and notebooks. > Read more about what is Kubeflow Cloud-native MLOps toolkit gets heavy To make Kubeflo

Canonical, the publisher of Ubuntu, releases Charmed Kubeflow, a set of charm operators to deliver the 20+ applications that make up the latest version of Kubeflow, for easy consumption anywhere, from workstations to on-prem, public cloud, and edge. > Visit Charmed-kubeflow.io to learn more. Kubeflow, the ML toolkit on K8s Kubeflow provid

New release, increased capabilities. After 6 months since the release of 1.0, Kubeflow releases a new version with increased capabilities. This new version has focused on improving ML Workflow Productivity, Isolation and Security, and GitOps. Here is a list of the enhanced features: Fairing and Kale (Kubeflow Automated pipeLines Engine) f

Kubeflow dojo by IBM IBM organizes a 2-day Kubeflow dojo to get you up to speed on Kubeflow. If you did not have a chance to attend, check out the videos and slides here: Kubeflow Dojo by IBM Pipelines webinar by Canonical Canonical hosts one webinar alongside blog series to demystify Kubeflow pipelines and help

This blog series is part of the joint collaboration between Canonical and Manceps. Visit our AI consulting and delivery services page to know more. Introduction Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the complexity and time involv

Kubeflow 101 – Hyperparameter tuning with Katib The Kubeflow 101 series of short videos is a great way to quickly get up to speed on Kubeflow concepts. This week, Stephanie Wong guides us through Hyperparameters and how you can use Katib to achieve the Kubeflow with Amazon Sagemaker Shashank Prasanna, Senior Developer Advocate at AWS

Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is one part of a larger Kubeflow ecosystem that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. In this blog series, we demystify Kubeflow pipelines and showcase this method to produce

Microsoft exposes attacks to Kubeflow deployments Microsoft publishes report detailing series of attacks against clusters running Kubeflow with the purpose of mining cryptocurrencies. To ensure that you are on the safe side, follow the steps below: 1. When deploying Kubeflow, make sure that its dashboard isn’t exposed to the internet: che

SageMaker Embraces Kubeflow Pipelines Amazon announced this week the possibility to configure Kubeflow Pipelines to run ML jobs with Amazon SageMaker. This is yet another validation of Kubeflow as a widespread solution and reinforces the idea of ML workflows on Kubernetes. Read the post here.