mlVajra

A framework or best practices to develop end to end machine learning pipeline (also has some tips for ML-management people ) Aim : To built robust depoyment pipeline strategies using Open source stack planning to add as many strategies in this repo pertaining to ML-deployment

Installation :

pip install mlvajra - only installs mlvajra binaries

To install complete dependencies: (for time being)

git clone https://github.com/rajagurunath/mlvajra.git

create virtualenv

virtualenv -p python3 vajra_env

source vajra_env\bin\activate

cd mlvajra
Install all required dependencies from repo

pip install -r requirements.txt

TODO list

Deploy

Mlflow
Tensorflow serving

model-Training /distribuited

mlflow -generic classification metrics (done)
nnictl-automl -tensorflow /pytorch

Feature Engineering

pandas
pyspark-Flint

preprocessing

cyclic features (done)
lag features
window features

Metadata

Release files for mlvajra 0.1.4.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for mlvajra 0.1.4.3
File	Interpreter	ABI	Platform	Reset
mlvajra-0.1.4.3-py2.py3-none-any.whl 2.4 MB Apr 22, 2019	Python 2, Python 3	none	any	Details

Release files / mlvajra-0.1.4.3-py2.py3-none-any.whl

Download URL	mlvajra-0.1.4.3-py2.py3-none-any.whl
Size	2.4 MB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`1e40defcb2e1d7b816de2cc657cf27936ae5ee4890e9013c686ba2afb2e1b0a7`
BLAKE2b-256 checksum How to use checksums	`24424dbc538b65874acf9629d02be0e0be30c2bd5880c0e6dcd8c768de4fcdc3`
Upload date	Apr 22, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`python-requests/2.18.4`