amy + kubeflow   63

jarokaz/mlops-labs
This repo manages a set of labs designed to demonstrate best practices and patterns for implementing and operationalizing production grade ML workflows on Google Cloud Platform.

With a few exceptions the labs are self-contained - they don't rely on other labs. The goal is to create a portoflio of labs that can be utilized in development and delivery of scenario specific demos and workshops.
machine_learning  kubeflow  ml_ops 
19 days ago by amy
SeldonIO/mlgraph: Machine Learning Inference Graph Spec
MLGraph defines a graph of machine learning components. The goal is to provide a simple machine learning focused specification for defining:

Easy model Experimentation and AB tests
Advanced routing with Multi-Armed Bandits
Ensembling of models
Explanations, Outlier Detection, Skew and Bias detection
Builds upon KFServing and other ML Serving Components
Flexible graph nodes:
References or inline specs
Custom user provided components *Auto-validation of graph
machine_learning  kubeflow  seldon 
7 weeks ago by amy
pipelines/TFX_pipeline.ipynb at master · kubeflow/pipelines
TFX Components
This notebook shows how to create pipeline that uses TFX components:

CsvExampleGen
StatisticsGen
SchemaGen
ExampleValidator
Transform
Trainer
Evaluator
kubeflow  machine_learning  tfx 
7 weeks ago by amy
Workload Identity  |  Kubernetes Engine Documentation  |  Google Cloud
Workload Identity is the recommended way to access Google Cloud services from within GKE due to its improved security properties and manageability.
kubernetes  kubeflow 
9 weeks ago by amy
An end-to-end ML pipeline on-prem: Notebooks & Kubeflow Pipelines on the new MiniKF
An end-to-end ML pipeline on-prem:
Notebooks & Kubeflow Pipelines on the new MiniKF
kubeflow 
august 2019 by amy
Securing Your Clusters | Kubeflow
How to secure Kubeflow clusters using VPC service controls and private GKE
kubeflow 
may 2019 by amy
StefanoFioravanzo/kale: Deploy Jupypter Notebooks to Kubeflow Pipelines
Deploy Jupypter Notebooks to Kubeflow Pipelines

Kale is a Python package that aims at automatically deploy a general purpose Jupyter Notebook as a running Kubeflow Pipelines instance, without requiring the use the specific KFP DSL.

The general idea of kale is to automatically arrange the cells included in a notebook, and transform them into a unified KFP-compliant pipeline. To do so, the user is only required to decide which cells correspond to which pipeline step, by the use of tags. In this way, a researcher can better focus on building and testing its code locally, and then scale it in a simple, organized and controlled way.
kubeflow  machine_learning  jupyter 
may 2019 by amy
pipelines/manifests at master · kubeflow/pipelines
This folder contains Kubeflow Pipelines Kustomize manifests for a light weight deployment. You can follow the instruction and deploy Kubeflow Pipelines in an existing cluster.
kubeflow  kfp  gcp  machine_learning 
may 2019 by amy
nuclio/nuclio: High-Performance Serverless event and data processing platform
High-Performance Serverless event and data processing platform
kubeflow 
may 2019 by amy
Twitter
RT : 0.5 is here! Updates to Fairing and the UI mean huge improvements to model development. Learn more & try…
Kubeflow  from twitter
april 2019 by amy
Twitter
RT : Using ML to label issues ; learn more from at…
kubeflow  from twitter
april 2019 by amy
MiniKF – arrikto
A production-ready, full-fledged, local Kubeflow deployment that installs in minutes.

MiniKF is the fastest and easiest way to get started with Kubeflow. With just a few clicks, you are up for experimentation, and for running complete Kubeflow Pipelines.

To train at scale, move to a Kubeflow cloud deployment with one click, without having to rewrite anything.

See here for the official announcement and why we started MiniKF.
k8s  kubernetes  kubeflow 
march 2019 by amy
Home - Acumos
Planning to bundle Kubeflow in next release:
Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation.

Acumos is part of the LF Deep Learning Foundation, an umbrella organization within The Linux Foundation that supports and sustains open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.
machine_learning  kubernetes  kubeflow  gcp 
december 2018 by amy
Kubeflow 0.4: Release Update & What’s Coming – kubeflow – Medium
RT : An update on our progress towards the Kubeflow 0.4 release, and key features to look forward to:

kubeflow  KubeCon  from twitter
december 2018 by amy

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