This article section’ll provide a step-by-step guide on deploying a web service to the Back4app platform. We’ll use Back4app’s no-code BaaS solution. If you’re interested in custom-code solutions, check out the following articles: Deploy a Flask Web Service to Back4app Containers De...
2. Create a REST API with Flask Now, we will create a Flask API that will be called later for inferencing. #importing necessary libraries from flask import Flask, jsonify, request import pandas as pd import joblib app = Flask(name)
git clone https://github.com/<github-user>/python-sample-vscode-flask-tutorial.git Replace <github-user> with the name of the GitHub account where you forked the repo. If you're using a different app repo, this repo is where you'll set up GitHub Actions.ห...
In ourlast post, we demonstrated how to develop a machine learning pipeline and deploy it as a web app using PyCaret and Flask framework in Python. If you haven’t heard about PyCaret before, please read thisannouncementto learn more. In this tutorial, we will use the same machine learning...
Here's a typical Docker build process: FROM ubuntu:12.04 RUN apt-get update && apt-get install -y python python-pip curl RUN curl -sSL https://github.com/shykes/helloflask/archive/master.tar.gz | tar -xzv RUN cd helloflask-master && pip install -r requirements.txt Note that Docker ...
tasks in our initial tutorial, we will quickly recap them and then focus on the remaining items in the list above. If you are interested in learning more about developing a machine learning pipeline in Python using PyCaret and building a web app using a Flask framework, please readthis ...
Running the app on our local computer (Cloud Shell) To run the Django app on our local computer, we need to set up aPython development environment, including Python, pip, and virtualenv. Create an isolated Python environment, and install dependencies: ...
使用Flask 和 Elastic Beanstalk 視覺化 AI/ML 模型結果 更多模式 大型主機 安裝 以 AWS 服務 從 IBM z/OS 存取 AWS CLI 將大型主機資料備份和封存至 Amazon S3 使用AWS Mainframe Modernization 和 建置 COBOL Db2 程式 AWS CodeBuild 建置Micro Focus Enterprise Server P...
使用Flask 和 Elastic Beanstalk 視覺化 AI/ML 模型結果 更多模式 大型主機 安裝 以 AWS 服務 從 IBM z/OS 存取 AWS CLI 將大型主機資料備份和封存至 Amazon S3 使用AWS Mainframe Modernization 和 建置 COBOL Db2 程式 AWS CodeBuild 建置Micro Focus Enterprise Server PAC ...
使用Flask 和 Elastic Beanstalk 查看人工智能/机器学习 (AI/ML) 模型结果 更多模式 大型机 通过安装 IBM z/OS 进行访问 AWS 服务 AWS CLI 备份大型机数据并将其存档至 Amazon S3 使用AWS Mainframe Modernization 和构建 COBOL Db2 程序 AWS CodeBuild ...