deploying-machine-learning-models | online course `` Deployment of Machine Learning Models | Machine Learning library

by trainindata Jupyter Notebook Version: 4.0.5 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | deploying-machine-learning-models Summary

deploying-machine-learning-models is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Docker applications. deploying-machine-learning-models has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

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deploying-machine-learning-models has a low active ecosystem.

It has 607 star(s) with 5815 fork(s). There are 30 watchers for this library.

It had no major release in the last 12 months.

There are 10 open issues and 5 have been closed. On average issues are closed in 95 days. There are 481 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deploying-machine-learning-models is 4.0.5

Quality

deploying-machine-learning-models has 0 bugs and 27 code smells.

Security

deploying-machine-learning-models has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deploying-machine-learning-models code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

deploying-machine-learning-models is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

deploying-machine-learning-models releases are available to install and integrate.

deploying-machine-learning-models saves you 467 person hours of effort in developing the same functionality from scratch.

It has 1101 lines of code, 74 functions and 49 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed deploying-machine-learning-models and discovered the below as its top functions. This is intended to give you an instant insight into deploying-machine-learning-models implemented functionality, and help decide if they suit your requirements.

Validate input data
Prepare dataframe
Remove missing values from input data
Remove errors from the validated input
Create a Config object from a parsed YAML specification
Find the configuration file
Load a YAML configuration file
Get a logger
Get a console handler
Get a file handler
Resize the image
Resize an image
Save a pipeline to disk
Removes Pipelines from the training directory
Predict a single image
Make a prediction for a single image
Create Flask application instance
Saves the pipeline keras to disk
Construct a CNN
Run training and test
Load a dataset

Get all kandi verified functions for this library.

deploying-machine-learning-models Key Features

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deploying-machine-learning-models Examples and Code Snippets

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Community Discussions

Trending Discussions on deploying-machine-learning-models

How to setup a request.py for this machine learning models?

QUESTION

How to setup a request.py for this machine learning models?

Asked 2019-May-26 at 07:14

I am following this online tutorial and deploying a machine learning models using Python. I have done all the parts as instructed, including having created the model.py, and request.py files, and run those in Terminal.

But, I failed to create a request.py file to generate the prediction. My server.py is:

...

ANSWER

Answered 2019-May-25 at 22:33

Isn't your server.py file is missing from flask import request as shown in line 2 in the tutorial?

Source https://stackoverflow.com/questions/56309214

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deploying-machine-learning-models

You can download it from GitHub.

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