Pankaj Kumar's Projects
GitHub Action that allows you to deploy machine learning models in Azure Machine Learning.
Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
Owner: Jacob Parr | Apache Spark Programming with Databricks
Code behind Arxiv Papers
A curated list of awesome Machine Learning frameworks, libraries and software.
A curated list of references for MLOps
A curated list of reinforcement learning with human feedback resources (continually updated)
Azure Quickstart Templates
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.
Docker containers for running training scripts on AzureML
Official community-driven Azure Machine Learning examples, tested with GitHub Actions
Scalable solution for ML Observability
The uncompromising Python code formatter
A command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, VueJS projects.
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
CLI tool for advanced Databricks jobs management.
Dendrite is a second-generation Matrix homeserver written in Go!
Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022)
Labs for Course DP-100: Designing and Implementing Data Science Solutions on Microsoft Azure
Feature Store for Machine Learning
Azure plugins for Feast (FEAture STore)
Feathr ā An Enterprise-Grade, High Performance Feature Store
Data Science Feature Engineering and Selection Tutorials
The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Hydra is a framework for elegantly configuring complex applications
The Interpret Community extends Interpret repository with additional interpretability techniques and utility functions to handle real-world datasets and workflows.
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