Alternatives and similar repositories for python-collab-template

Users that are interested in python-collab-template are comparing it to the libraries listed below

• eugeneyan / papermill-mlflow
  🧪 Simple data science experimentation & tracking with jupyter, papermill, and mlflow.
  ☆182Updated last year
• eugeneyan / testing-ml
  🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.
  ☆264Updated 2 years ago
• outerbounds / dsbook
  Code samples for the Effective Data Science Infrastructure book
  ☆115Updated 2 years ago
• edublancas / ml-testing
  🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects
  ☆81Updated 3 years ago
• shreyashankar / toy-ml-pipeline
  Toy example of an applied ML pipeline for me to experiment with MLOps tools.
  ☆209Updated 3 years ago
• eugeneyan / applyingml
  📌 Papers, guides, and mentor interviews on applying machine learning for ApplyingML.com—the ghost knowledge of machine learning.
  ☆203Updated last year
• trainindata / testing-and-monitoring-ml-deployments
  Example project for the course "Testing & Monitoring Machine Learning Model Deployments"
  ☆136Updated last year
• vopani / datatableton
  100 exercises to learn Python Datatable
  ☆269Updated 3 years ago
• dslp / dslp-repo-template
  Template repository for data science lifecycle project
  ☆195Updated 5 years ago
• mlops-guide / dvc-gitactions
  Example project with a complete MLOps cycle: versioning data, generating reports on pull requests and deploying the model on releases wit…
  ☆48Updated 3 years ago
• jacopotagliabue / FREE_7773
  Materials for my 2021 NYU class on NLP and ML Systems (Master of Engineering).
  ☆96Updated 2 years ago
• PacktPublishing / Practical-Deep-Learning-at-Scale-with-MLFlow
  Practical Deep Learning at Scale with MLFlow, published by Packt
  ☆161Updated last year
• priceloop / conventions
  ∞ Priceloop Engineering Conventions for Scala, Python, Git Workflow etc
  ☆100Updated 2 years ago
• jacopotagliabue / recs-at-resonable-scale
  Recommendations at "Reasonable Scale": joining dataOps with recSys through dbt, Merlin and Metaflow
  ☆238Updated 2 years ago
• Machine-Learning-Tokyo / MLT_starterkit
  ☆92Updated 4 years ago
• eugeneyan / poc-docker-template
  Simple template showing how to set up docker for reproducible data science with Jupyter notebooks.
  ☆23Updated last year
• tgoldenberg / kedro-mlflow-example
  ☆44Updated 2 years ago
• khuyentran1401 / customer_segmentation
  An end-to-end project on customer segmentation
  ☆83Updated 2 years ago
• jacopotagliabue / post-modern-stack
  Joining the modern data stack with the modern ML stack
  ☆198Updated 2 years ago
• visenger / Awesome-ML-Model-Governance
  This repository provides a curated list of references about Machine Learning Model Governance, Ethics, and Responsible AI.
  ☆116Updated last year
• mihail911 / fake-news
  Building a fake news detector from initial ideation to model deployment
  ☆155Updated this week
• noahgift / mlops-duke-aipi
  Syllabus for Artificial Intelligence for Product Innovation Master of Engineering: https://ai.meng.duke.edu/degree
  ☆32Updated 2 years ago
• khuyentran1401 / Machine-learning-pipeline
  Example machine learning pipeline with MLflow and Hydra
  ☆91Updated 2 years ago
• GokuMohandas / feature-store
  Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.
  ☆57Updated 2 years ago
• zenml-io / zenml-gitflow
  A repository that showcases how you can use ZenML with Git
  ☆69Updated 2 months ago
• GokuMohandas / testing-ml
  Learn how to create reliable ML systems by testing code, data and models.
  ☆88Updated 2 years ago
• dmatrix / mlflow-workshop-part-1
  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 4 years ago
• data-max-hq / ab-testing-in-ml
  Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.
  ☆40Updated 6 months ago
• GokuMohandas / monitoring-ml
  Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.
  ☆88Updated 2 years ago
• rxavier / poniard
  Streamline scikit-learn model comparison.
  ☆144Updated 2 years ago