I’m a management consultant and blogger who loves using data science and machine learning to solve real-world problems
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Versioning-ML-Models-datasets-With-DVC
June 12, 2021 – June 23, 2021
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.
View ProjectBuild-ML-Models-As-Rest-API
May 25, 2021 – June 17, 2021
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.
View ProjectReproducible-ML-Reports-with-YAML-Configs
May 23, 2021 – May 24, 2021
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.
View ProjectBring-DevOps-to-Machine-Learning-with-CML
April 14, 2021 – September 30, 2021
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.
View ProjectAutomated-Testing-With-Github-Actions
April 10, 2021 – September 29, 2021
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.
View ProjectData-Apps
October 25, 2020 – Present
A collection of application which are built on open source technologies/frameworks like R Shiny, Plotly-Dash, Flask and Streamlit
View ProjectBlogs
October 9, 2020 – Present
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
View ProjectInteractive-Modelling-with-Shiny
October 7, 2020 – January 28, 2022
R Shiny to build an app for data exploration, interactive model building app, identifying variable importance and predicting
View Projectamitvkulkarni.github.io
September 22, 2020 – Present
amitvkulkarni.github.io — GitHub repository
View ProjectProjectManagement
March 14, 2020 – May 24, 2021
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.
View ProjectCultural Fit Analysis
The candidate's personal projects demonstrate a strong initiative and self-driven learning, which aligns well with a culture of continuous improvement. The diversity of projects, from MLOps to data visualization and web applications, indicates a broad interest in the data science ecosystem. The emphasis on open-source technologies and sharing knowledge through blogs suggests a collaborative mindset. However, the lack of team-based projects or professional experience makes it difficult to fully assess cultural fit in a corporate environment.
Soft Skills & Operational Fit
The candidate's project descriptions suggest an interest in automation, collaboration, and reproducible workflows, which are positive indicators for operational fit. The focus on MLOps and CI/CD implies an understanding of efficient development practices. However, without specific assessment data on communication, logical reasoning, or teamwork, a definitive assessment of soft skills and operational fit is limited.