Skip to main content

A Last weapon to save Data scientist

Project description


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

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


  • Mlflow
  • Tensorflow serving

model-Training /distribuited

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

Feature Engineering

  • pandas
  • pyspark-Flint


  • cyclic features (done)
  • lag features
  • window features

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for mlvajra, version
Filename, size File type Python version Upload date Hashes
Filename, size mlvajra- (2.4 MB) File type Wheel Python version py2.py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page