I won’t lie — there’s no coincidence that I introduce Kubeflow right after writing that tools can be difficult to be deployed. Ladies and gentlemen, meet Kubeflow — one of the most popular and yet one of the most irritating tools I used in years (CMake, brother, I will never forget you).
Kubeflow is one of the hottest things in ML and MLOps area recently with around 30 actively developed repositories with almost 20,000 stars from GitHub users. Does “The Machine Learning Toolkit for Kubernetes” make you think that this could be and do literally everything? Well, then you got it quite right. Kubeflow has few key components:
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
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
Data analytics is only for IT people, right? Wrong!!
Recently I was working for a CFO of a drinks company who couldn’t master anything to do with data or Excel. Problem was, all of his figures were produced in Excel by his staff (who didn’t speak English- or at least pretended not to). Obviously, everyone was cheating. But he had to sign off the accounts, or face fines… but if he signed off the wrong accounts, he could face – prison?
But you might be thinking that you cannot possibly get into data analytics now because you don’t have time. I get you. I’ve been living in Vietnam for five years and, although, it would be much better experience if I spoke Vietnamese, I don’t. So, what do you do? Well, you get dashboards. Ok, ok, but consulting companies are always trying to sell you dashboards for lots of money, and since you don’t know much about data analytics, you aren’t sure who is ripping you off more.
So, a little time spent understanding the basics can help you to choose the right vendors, understand the solutions that are necessary and be aware of the risks that you are facing every day.
#big data #latest news #data analytics for dummies #data analytics #dummies
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In this kubeflow: machine learning on kubernetes video you will learn introduction to kubeflow, who built kubeflow, how will kubeflow help, kubeflow+kubernetes, kubeflow and machine learning with kubeflow launch in detail.
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#machinelearningonkubernetes #machinelearningwithkubeflow #introductiontokubeflow #kubeflowtutorial #kubeflowtraining #kubeflow
Open-source machine learning toolkit built for Kubernetes, Kubeflow has now released its 1.2 version. Some of the major features in this new version include enhancements to the model building, training tuning, and machine learning pipelining; introduction of automated configuration of hyperparameters for better accuracy; serverless interfacing on Kubernetes; and providing interactive coding environment for better model development. Some of the important features are discussed below.
A Kubernetes-based system for hyperparameter tuning and neural architecture search, Katib supports several machine learning frameworks such as TensorFlow, XGBoost, and Pytorch.
The new version of Kubeflow introduces an upgraded Katib with v1bet1 AP1 for ensuring delivery of more accurate models using better infrastructure utilisation. It will be achieved through the automated configuration of hyperparameters (variables that control the model training process). A few of the important features of Katib 0.10 include:
#developers corner #kubeflow 1.2 #kubeflow new release #kubernetes #release features