IBM / data-science-best-practicesLinks
The goal of this repository is to enable data scientists and ML engineers to develop data science use cases and making it ready for production use. This means focusing on the versioning, scalability, monitoring and engineering of the solution.
☆92Updated 4 months ago
Alternatives and similar repositories for data-science-best-practices
Users that are interested in data-science-best-practices are comparing it to the libraries listed below
Sorting:
- Template repository for data science lifecycle project☆196Updated 5 years ago
- Machine Learning Engineering with Python☆185Updated last week
- Partly lecture and partly a hands-on tutorial and workshop, this is a three part series on how to get started with MLflow. In this three …☆240Updated 5 years ago
- Engineering MLOps, published by Packt☆188Updated last week
- Machine Learning Engineering with MLflow, published by Packt☆117Updated last week
- Tutorials on creating a reproducible and maintainable data science project☆148Updated 3 years ago
- 🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.☆182Updated last year
- This repository provides a curated list of references about Machine Learning Model Governance, Ethics, and Responsible AI.☆117Updated last year
- Construct a modern data stack and orchestration the workflows to create high quality data for analytics and ML applications.☆226Updated 3 years ago
- Practical Deep Learning at Scale with MLFlow, published by Packt☆162Updated last week
- This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.☆416Updated 8 months ago
- An end-to-end project on customer segmentation☆83Updated 2 years ago
- An example MLFlow project☆48Updated 8 months ago
- 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions.☆149Updated last year
- Code base for programming projects☆55Updated 2 weeks ago
- Data Science Standards - Framework to Productionize Projects throughout the Data Science and Production Solution Lifecycle☆44Updated 3 years ago
- Demo repository implementing an end-to-end MLOps workflow on Databricks, using Azure DevOps for CICD orchestration. Project derived from …☆31Updated 3 years ago
- ☆64Updated last year
- Reference code base for ML Engineering, Manning Publications☆132Updated 4 years ago
- Applied Machine Learning Explainability Techniques, published by Packt☆246Updated last week
- Useful data science and Python code snippets at Data Science Simplified☆72Updated 4 years ago
- An example MLflow project☆271Updated last year
- Capturing model drift and handling its response - Example webinar☆108Updated 6 years ago
- Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications☆102Updated 2 years ago
- Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in productio…☆95Updated last year
- End to end MLRun demos☆93Updated last month
- A book of subtle code tricks and gem resources for all things data, machine learning and deep learning.☆168Updated last year
- Example project for the course "Testing & Monitoring Machine Learning Model Deployments"☆136Updated last year
- ML project template facilitating both research and production phases.☆111Updated 6 years ago
- ☆79Updated 2 years ago