Alternatives and similar repositories for PyTestforDataScience_PyDataLA:

Users that are interested in PyTestforDataScience_PyDataLA are comparing it to the libraries listed below

• outerbounds / full-stack-ML-metaflow-tutorial
  It's all in the name
  ☆77Updated last year
• tdpetrou / integrating-pandas-with-scikit-learn
  Tutorial covering a new workflow available going from pandas to scikit-learn
  ☆40Updated 2 years ago
• ryanmark1867 / deep_learning_for_structured_data
  Production repo to accompany Deep Learning with Structured Data book from Manning: https://www.manning.com/books/deep-learning-with-struc…
  ☆73Updated 3 years ago
• amueller / aml
  Applied Machine Learning with Python
  ☆78Updated last year
• StevenMMortimer / ge-sklearn-pipeline-example
  Example using Great Expectations to Validate Data in a scikit-learn Pipeline
  ☆21Updated 4 years ago
• DunderData / Tutorials
  Python data science and machine learning from Ted Petrou with Dunder Data
  ☆55Updated 2 years ago
• tklouie / PyData_LA_2018
  Added repo for PyData LA 2018 tutorial
  ☆88Updated 6 years ago
• htorrence / pytest_examples
  Reference package for unit tests
  ☆49Updated 6 years ago
• KishManani / PyDataLondon2022
  Slides for "Feature engineering for time series forecasting" talk
  ☆59Updated 2 years ago
• solegalli / Python-Feature-Engineering-Cookbook-First-Edition
  ☆31Updated 10 months ago
• xLaszlo / CQ4DS-notebook-sklearn-refactoring-exercise
  ☆30Updated 2 years ago
• harnav / pydata-docker-tutorial
  Tutorial given at PyData LA 2018
  ☆97Updated 8 months ago
• osin-vladimir / mlflow_tutorial
  Managing machine learning life-cycle with MLflow tutorial
  ☆23Updated last year
• klemag / pydataLDN_2019-maintainable-code-for-data-science
  ☆47Updated 5 years ago
• MaartenGr / UnitTesting
  Guide for applying Unit Testing in data-driven projects
  ☆19Updated 4 years ago
• deliveryhero / pyconde2019-airflow-ml-workshop
  PyConDE & PyData Berlin 2019 Airflow Workshop: Airflow for machine learning pipelines.
  ☆47Updated last year
• drivendataorg / nbautoexport
  Automatically export Jupyter notebooks to various file formats (.py, .html, and more) on save.
  ☆77Updated last year
• trallard / airflow-tutorial
  🐍💨 Airflow tutorial for PyCon 2019
  ☆86Updated 2 years ago
• tdpetrou / Advent-of-Code-Pandas
  ☆22Updated 2 years ago
• trainindata / testing-and-monitoring-ml-deployments
  Example project for the course "Testing & Monitoring Machine Learning Model Deployments"
  ☆134Updated 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
• datarobot-community / examples-for-data-scientists
  ☆63Updated 11 months ago
• monte-carlo-data / data-downtime-challenge
  ☆84Updated 2 years ago
• amueller / dabl
  Data Analysis Baseline Library
  ☆131Updated 6 months ago
• schmidtbri / simple-ml-model-abc
  Code demonstrating a simple Machine Learning model abstract base class and its uses.
  ☆14Updated last year
• mdh266 / AirflowETL
  Blog post on ETL pipelines with Airflow
  ☆23Updated 4 years ago
• noahgift / databricks-zero-to-mlops
  ☆27Updated 2 years ago
• chandu-muthyala / Data-Engineer-Nano-Degree
  Data models, build data warehouses and data lakes, automate data pipelines, and worked with massive datasets.
  ☆13Updated 5 years ago
• DiegoUsaiUK / Propensity_Modelling
  Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
  ☆27Updated 2 years ago
• juanitorduz / ml_prod_tutorial
  Explore tips and tricks to deploy machine learning models with Docker.
  ☆13Updated last year