opeyemibami / TDD-in-MLOpsLinks
This repo is an approach to TDD in machine learning model operation. it covers project structure, testing essentials using pytest with Git automation and other latest tools.
β15Updated 5 years ago
Alternatives and similar repositories for TDD-in-MLOps
Users that are interested in TDD-in-MLOps are comparing it to the libraries listed below
Sorting:
- Powerful rapid automatic EDA and feature engineering library with a very easy to use API πβ53Updated 3 years ago
- Best practices for engineering ML pipelines.β36Updated 3 years ago
- π Material for PyData Global 2021 Presentation: Effective Testing for Machine Learning Projectsβ82Updated 3 years ago
- Instant search for and access to many datasets in Pyspark.β34Updated 3 years ago
- Enterprise Solution for Text Classification (using BERT)β10Updated 2 years ago
- Projects developed by Domino's R&D teamβ77Updated 3 years ago
- Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.β40Updated 11 months ago
- β19Updated 4 years ago
- β16Updated 4 years ago
- ForML - A development framework and MLOps platform for the lifecycle management of data science projectsβ106Updated 2 years ago
- β21Updated 2 years ago
- β12Updated 5 years ago
- Strategies to deploy deep learning modelsβ27Updated 7 years ago
- Slides and notebook for the workshop on serving bert models in productionβ25Updated 3 years ago
- β22Updated 3 years ago
- β18Updated 4 years ago
- β21Updated 4 years ago
- Machine Learning Projects with Flytekitβ36Updated 2 years ago
- A PaaS End-to-End ML Setup with Metaflow, Serverless and SageMaker.β37Updated 4 years ago
- Examples to showcase the use of Kale in data science pipelinesβ27Updated 2 years ago
- Demo on how to use Prefect with Dockerβ27Updated 3 years ago
- Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlibβ19Updated 4 years ago
- Hypergol is a Data Science/Machine Learning productivity toolkit to accelerate any projects into production with autogenerated code, stanβ¦β53Updated 2 years ago
- real-time data + ML pipelineβ53Updated last week
- A simple search engine to search medium stories built with streamlit and elasticsearch.β40Updated 3 years ago
- Using Kafka-Python to illustrate a ML production pipelineβ112Updated 3 years ago
- β30Updated last year
- Know your ML Score based on Sculley's paperβ34Updated 6 years ago
- Dvc + Streamlit = β€οΈβ40Updated 2 years ago
- 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 four pβ¦β35Updated 5 years ago