liorsav commented on a change in pull request #57: URL: https://github.com/apache/incubator-liminal/pull/57#discussion_r673936699
########## File path: docs/getting-started/iris_classification.md ########## @@ -0,0 +1,274 @@ +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +# Getting started / ***Iris Classification*** + +This guide will allow you to set up your first Apache Liminal environment and allow you to create +some simple ML pipelines. These will be very similar to the ones you are going to build for real +production scenarios. + +## Prerequisites + +Python 3 (3.6 and up) + +[Docker Desktop](https://www.docker.com/products/docker-desktop) + +*Note: Make sure kubernetes cluster is running in docker desktop (or custom kubernetes installation +on your machine).* + Review comment: Please add this section: Example flow: We will define the following steps and services to implement the Iris classification example: Train, Validate & Deploy - Training and validation execution is managed by Liminal Airflow extension. The training task trains a regression model using a public dataset. We then validate the model and deploy it to a model-store in mounted volume. Inference - online inference is done using a Python Flask service running on the local Kubernetes in docker desktop. The service exposes the /predict endpoint. It reads the model stored in the mounted drive and uses it to evaluate the request. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
