Alternatives and similar repositories for ml-pipeline-engineering

Users that are interested in ml-pipeline-engineering are comparing it to the libraries listed below

• data-max-hq / ab-testing-in-ml
  Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.
  ☆40Updated 8 months 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
• elleobrien / automatic_testing
  ☆21Updated 2 years ago
• Spratiher9 / Sparkora
  Powerful rapid automatic EDA and feature engineering library with a very easy to use API 🌟
  ☆53Updated 3 years ago
• jrzaurin / ml-pipeline
  Using Kafka-Python to illustrate a ML production pipeline
  ☆112Updated 2 years ago
• deliveryhero / pyconde2019-airflow-ml-workshop
  PyConDE & PyData Berlin 2019 Airflow Workshop: Airflow for machine learning pipelines.
  ☆47Updated 2 years ago
• thejat / mlops-notebooks
  ☆66Updated 5 months ago
• ahmedbesbes / mlflow
  Introduction to MLflow with a demo locally and how to set it on AWS
  ☆42Updated 4 years ago
• npatta01 / pytorch-serving-workshop
  Slides and notebook for the workshop on serving bert models in production
  ☆26Updated 2 years ago
• formlio / forml
  ForML - A development framework and MLOps platform for the lifecycle management of data science projects
  ☆106Updated 2 years ago
• saturncloud / saturn-cloud-examples
  ☆19Updated 4 years ago
• jacopotagliabue / no-ops-machine-learning
  A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.
  ☆37Updated 4 years ago
• GokuMohandas / feature-store
  Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.
  ☆57Updated 3 years ago
• ds-muzalevskiy / sagemaker-docker-deploy
  ☆18Updated 3 years ago
• opeyemibami / TDD-in-MLOps
  This repo is an approach to TDD in machine learning model operation. it covers project structure, testing essentials using pytest with Gi…
  ☆15Updated 4 years ago
• jacopotagliabue / FREE_7773
  Materials for my 2021 NYU class on NLP and ML Systems (Master of Engineering).
  ☆96Updated 2 years ago
• dkhundley / ds-quick-tips
  Data Science Quick Tips Repository!
  ☆48Updated last year
• edublancas / ml-testing
  🐍 Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projects
  ☆82Updated 3 years ago
• chiphuyen / metaflow-transformers-tutorials
  Metaflow tutorials for ODSC West 2021
  ☆64Updated 3 years ago
• khuyentran1401 / prefect-docker
  Demo on how to use Prefect with Docker
  ☆27Updated 3 years ago
• aromain / intermarche_challenge
  [Intemarché] Sales forecasting challenge
  ☆11Updated 4 years ago
• hanneshapke / Deploying-Deep-Learning-Models
  Strategies to deploy deep learning models
  ☆27Updated 7 years ago
• priceloop / conventions
  ∞ Priceloop Engineering Conventions for Scala, Python, Git Workflow etc
  ☆100Updated 2 years ago
• GokuMohandas / monitoring-ml
  Learn how to monitor ML systems to identify and mitigate sources of drift before model performance decay.
  ☆92Updated 3 years ago
• schmidtbri / simple-ml-model-abc
  Code demonstrating a simple Machine Learning model abstract base class and its uses.
  ☆14Updated 2 years ago
• MaartenGr / ML-API
  Guide on creating an API for serving your ML model
  ☆67Updated 3 years ago
• G-Hung / model-productization_article
  This is repo to demonstrate how to convert from Jupyter Notebook to scripts with some engineering practices
  ☆84Updated 2 years ago
• outerbounds / dsbook
  Code samples for the Effective Data Science Infrastructure book
  ☆115Updated 2 years ago
• FavioVazquez / ds-optimus
  How to do data science with Optimus, Spark and Python.
  ☆19Updated 6 years ago
• asifahmed90 / pyspark-ML-in-Colab
  Pyspark in Google Colab: A simple machine learning (Linear Regression) model
  ☆38Updated 6 years ago