Alternatives and similar repositories for simple-ml-model-abc

Users that are interested in simple-ml-model-abc are comparing it to the libraries listed below

• canyon289 / PyTestforDataScience_PyDataLA
  Pytest for Data Science Beginners
  ☆59Updated 7 years ago
• jem1031 / pandas-pipelines-custom-transformers
  Material for Talk at PyData Seattle 2017
  ☆167Updated 7 years ago
• schmidtbri / ml-model-abc-improvements
  Code showing how to add metadata and versioning to an ML model base class.
  ☆11Updated 2 years ago
• jrzaurin / ml-pipeline
  Using Kafka-Python to illustrate a ML production pipeline
  ☆112Updated 2 years ago
• bodywork-ml / ml-pipeline-engineering
  Best practices for engineering ML pipelines.
  ☆36Updated 3 years ago
• MaartenGr / ML-API
  Guide on creating an API for serving your ML model
  ☆67Updated 3 years ago
• amueller / aml
  Applied Machine Learning with Python
  ☆80Updated last year
• chrispaulca / waterfall
  An easy to use waterfall chart function for Python
  ☆162Updated 4 years ago
• htorrence / pytest_examples
  Reference package for unit tests
  ☆49Updated 7 years ago
• tgoldenberg / kedro-mlflow-example
  ☆44Updated 2 years ago
• bgweber / DS_Production
  ☆155Updated 5 years ago
• WillKoehrsen / probabilistic-programming
  Experimenting with and teaching probabilistic programming
  ☆105Updated 3 years ago
• ds-muzalevskiy / sagemaker-docker-deploy
  ☆18Updated 4 years ago
• dziganto / Data_Science_Fundamentals
  ☆101Updated 7 years ago
• Ashton-Sidhu / aethos
  Automated Data Science and Machine Learning library to optimize workflow.
  ☆105Updated 2 years ago
• amueller / dabl
  Data Analysis Baseline Library
  ☆133Updated last year
• ericmjl / data-testing-tutorial
  A short tutorial for data scientists on how to write tests for code + data.
  ☆120Updated 5 years ago
• joelcthomas / modeldrift
  Capturing model drift and handling its response - Example webinar
  ☆108Updated 6 years ago
• hackalog / bus_number
  Up Your Bus Number: A Primer for Reproducible Data Science
  ☆69Updated 6 years ago
• edublancas / ml-testing
  🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects
  ☆82Updated 3 years ago
• deliveryhero / pyconde2019-airflow-ml-workshop
  PyConDE & PyData Berlin 2019 Airflow Workshop: Airflow for machine learning pipelines.
  ☆47Updated 2 years ago
• klemag / PyconUS_2019-model-interpretability-tutorial
  Content for the Model Interpretability Tutorial at Pycon US 2019
  ☆41Updated last year
• ianozsvald / dtype_diet
  Tries to shrink your Pandas column dtypes with no data loss so you have more spare RAM
  ☆84Updated last year
• harnav / pydata-docker-tutorial
  Tutorial given at PyData LA 2018
  ☆97Updated last year
• tdpetrou / integrating-pandas-with-scikit-learn
  Tutorial covering a new workflow available going from pandas to scikit-learn
  ☆39Updated 2 years ago
• AlexIoannides / ml-workflow-automation
  Python Machine Learning (ML) project that demonstrates the archetypal ML workflow within a Jupyter notebook, with automated model deploym…
  ☆64Updated 2 years ago
• AileenNielsen / OReillyHealthcareData
  ☆39Updated 8 years ago
• artefactory / one-click-mlflow
  A tool to deploy a mostly serverless MLflow tracking server on a GCP project with one command
  ☆71Updated 5 months ago
• osin-vladimir / mlflow_tutorial
  Managing machine learning life-cycle with MLflow tutorial
  ☆23Updated 2 years ago
• priceloop / conventions
  ∞ Priceloop Engineering Conventions for Scala, Python, Git Workflow etc
  ☆100Updated 3 years ago