Amit Kulkarni (@amitvkulkarni)
github

amitvkulkarni

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Top repositories

1

Data-Apps

A collection of application which are built on open source technologies/frameworks like R Shiny, Plotly-Dash, Flask and Streamlit
Python
52
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2

Blogs

Data science blogs & guides in python and R. The contents covers wide range of topics like MLOps, automation, simulations, visualizations, machine learning models, financial analysis, value investing and quantitative investing
Jupyter Notebook
15
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3

ProjectManagement

This application was built and submitted as part of R Shiny competition 2020. It can be used for creation of projects and related tasks. The user will be able to carry out all the basic CRUD operations on the data and save the changes.
R
11
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4

Versioning-ML-Models-datasets-With-DVC

Data Version Control, or DVC, is a data and ML experiment management tool that takes advantage of the existing engineering toolset that we are familiar with (Git, CI/CD, etc.). DVC is meant to be run alongside Git. The git and dvc commands will often be used in tandem, one after the other. While Git is used to storing and version code, DVC does the same for data and model files.
HTML
10
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5

Interactive-Modelling-with-Shiny

R Shiny to build an app for data exploration, interactive model building app, identifying variable importance and predicting
R
6
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6

Build-ML-Models-As-Rest-API

Example of building a Flask REST API for a classifier model. The same process can be applied to other machine learning or deep learning models once you have trained and saved them.
Python
5
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7

Bring-DevOps-to-Machine-Learning-with-CML

Leveraging the powerful features of DevOps like CI/CD, automation, workflows and apply them to our data science projects & experiments with MLOps. The CML – Continuous Machine Learning is a very handy tool have for tracking the experiment results, collaborate with others, and automating the entire workflow.
Python
3
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8

Automated-Testing-With-Github-Actions

Exploring features of Pytest / GitHub actions / vscode and how easy it is to automate many of the routine data-related activities that are carried out day in day out. One can also use a more sophisticated cloud platform with advanced features which let you achieve similar results with automation but, if you have a smaller team, a limited budget, and a shortage of test automation skills then Pytest / GitHub is more than handy to accomplish your project objectives.
Python
2
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9

Reproducible-ML-Reports-with-YAML-Configs

we will explore the process of building and managing machine learning reports using the configuration files and generate HTML reports. For this simple machine learning project, I will use the Breast Cancer Wisconsin (Diagnostic) Data Set. The objective of this ML project is to predict whether a person has a benign or malignant tumor.
Jupyter Notebook
2
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