Machine Learning Deployment Tutorials
Launch machine learning models into production using flask, docker etc.
Citing
If you find this code useful in your research, please consider citing the blog:
@misc{sagardeploy,
Author = {Abhinav Sagar},
Title = {How to Easily Deploy Machine Learning Models Using Flask},
Year = {2019},
Journal = {Towards Data Science},
}
1. Predict Sales
Check out the corresponding medium blog post https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4.
Environment and tools
- scikit-learn
- pandas
- numpy
- flask
Installation
pip install scikit-learn pandas numpy flask
python model.py
python app.py
2. Predict House Prices
Download the dataset from here.
Environment and tools
- scikit-learn
- pandas
- numpy
- flask
- docker
Installation
docker-compose up --build
curl -X POST -H "Content-Type: application/json" -d @to_predict_json.json http://localhost:8080/predict_price
where to_predict.json contains:
{"grade":9.0,"lat":37.45,"long":12.09,"sqft_living":1470.08,"waterfront":0.0,"yr_built":2008.0}
or
curl -X POST -H "Content-Type: application/json" -d '{"grade":9.0,"lat":37.45,"long":12.09,"sqft_living":1470.08,"waterfront":0.0,"yr_built":2008.0}' http://localhost:8080/predict_price
Output:
{
"predict cost": 1022545.34768284
}
License
MIT License
Copyright (c) 2019 Abhinav Sagar
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
-
小硕一枚，自己很水，目前在一家很大的公司做一枚小螺丝钉，已经工作一年，这一年的时间基本做的都是打杂的事情，所以痛下决心，决定改行跳槽，最近机器学习这么火爆，因此决定入坑。现在整理了一下自己的学习计划，为了督促自己更好的学习，所以打算把计划写下来。 按照时间来做计划： 2018.4.21--2018.5.31：学习书籍《机器学习实战》+《统计学方法》，争取能够达到对机器学习有一个初步理解
-
http://www.inf.ed.ac.uk/teaching/courses/iaml/ 转载于:https://www.cnblogs.com/caijinlong/archive/2013/05/04/3060012.html
-
Machine Learning Explainability for External Stakeholders Umang Bhatt12 McKane Andrus1 Adrian Weller2 3 Alice Xiang1 arXiv:2007.05408v1 [cs.CY] 10 Jul 2020 Abstract As machine learning is increasingly
-
学习意味着通过学习或经验获得知识或技能。 基于此，我们可以定义机器学习（ML）如下 - 它可以被定义为计算机科学领域，更具体地说是人工智能的应用，其为计算机系统提供了学习数据和从经验改进而无需明确编程的能力。 基本上，机器学习的主要焦点是允许计算机自动学习而无需人为干预。 现在问题是如何开始和完成这种学习？ 它可以从数据的观察开始。 数据可以是一些示例，指令或一些直接经验。 然后在此输入的基础上，
-
Machine Learning This project provides a web-interface,as well as a programmatic-apifor various machine learning algorithms. Supported algorithms: Support Vector Machine (SVM) Support Vector Regressio
-
深度学习 我们可以在Personal Computer上完成庞大的任务 深度学习是一种适应于各类问题的万能药 神经网络 神经网络出现于80年代，但当时计算机运行慢，数据集很小，神经网络不适用 现在神经网络回来了，因为能够进行GPU计算，可用使用的数据集也变大 分类 分类的一些讨论可以在这个项目里看到 Machine Learning不仅是Classification！但分类是机器学习的核心。 学会
-
Machine Learning Projects This repository contains mini projects in machine learning with jupyter notebook files.Go to the projects folder and see the readme for detailed instructions about the projec
-
Machine Learning for OpenCV This is the Jupyter notebook version of the following book: Michael Beyeler Machine Learning for OpenCV Intelligent Image Processing with Python 14 July 2017 Packt Publishi
-
Machine Learning and Data Science Applications in Industry Sov.ai Research Lab (Sponsorship) Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data S