serverless-machine-learning | deploy serverless Machine Learning Microservice | Serverless library

by patrick-michelberger Python Version: Current License: No License

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

kandi X-RAY | serverless-machine-learning Summary

serverless-machine-learning is a Python library typically used in Serverless, Docker applications. serverless-machine-learning has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This repository accompanies my blog post How to deploy a Serverless Machine Learning Microservice with AWS Lambda, AWS API Gateway and scikit-learn.

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serverless-machine-learning has a low active ecosystem.

It has 41 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 378 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of serverless-machine-learning is current.

Quality

serverless-machine-learning has 0 bugs and 0 code smells.

Security

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

serverless-machine-learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

serverless-machine-learning does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

serverless-machine-learning releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

serverless-machine-learning saves you 15 person hours of effort in developing the same functionality from scratch.

It has 44 lines of code, 5 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Get prediction
Load a model from S3
Predict a model

Get all kandi verified functions for this library.

serverless-machine-learning Key Features

No Key Features are available at this moment for serverless-machine-learning.

serverless-machine-learning Examples and Code Snippets

No Code Snippets are available at this moment for serverless-machine-learning.

Community Discussions

Trending Discussions on serverless-machine-learning

How to import trained machine learning model from AI Patform into cloud functions in python

QUESTION

How to import trained machine learning model from AI Patform into cloud functions in python

Asked 2019-Oct-18 at 11:49

I have a trained machine learning model to predict one metric of my data in firestore.

I have inserted data into bigquery to train the model and ones it is trained I want to deploy it to make predictions and insert this predictions again in firestore.

I have been reading a lot how to do this and finally I have found a way to do it: https://angularfirebase.com/lessons/serverless-machine-learning-with-python-and-firebase-cloud-functions/

I have a few questions of this guide:

In the 4th step of this guide he save the model in firebase_admin storage. Why in firebase_admin storage and not in a google cloud storage?

He deploy the model in Google cloud ML engine. Why he do that? what advantadges I have to deploy the model there instead of save the model in Google cloud storage and after that call it in cloud functions? or it is necessary to do this step?

Once he deploy this model in Google cloud ML engine I can call it in a cloud fuction in python to run the model whatever the trigger I choose?

...

ANSWER

Answered 2019-Oct-18 at 11:49

1) You can use Cloud Storage, as well. In the tutorial he suggests a way of achieving something, but it is not the single absolute way.

2) You can also do what you have said, but the way from that tutorial is better, I would say. You have the advantage of exposing your model as an API endpoint. This way you will have less work to do in the Cloud Functions code which is good.

3) I am not sure if I understood this question. You should use a HTTP trigger since you are trying to hit an API endpoint.

Source https://stackoverflow.com/questions/58443863

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

Vulnerabilities

No vulnerabilities reported

Install serverless-machine-learning

You can download it from GitHub.
You can use serverless-machine-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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