Machine Learning¶
Machine Learning Tooling GitHub space with ranked lists of awesome Python libraries for, updated weekly.
- GUI for ML workflow and pipeline discovery
- ML prototypes
- Designing intelligence
- AI, ML and DL
- Game theory for ML interpretation
pycaret- Hybrid rule based ML
pycaret-2.0- QLattice
- Applied ML use cases
- Google ML glossary
- 130 ML Tricks And Resources Carefully Curated
- Geomstats: a Python package for computations, statistics, machine learning and deep learning on manifolds
- LitServe: an easy-to-use, flexible serving engine for AI models built on FastAPI
- Causality in ML Models: Introducing Monotonic Constraints
Model evaluation¶
- Plot learning curve
- Validation sets
- ML Tool
- Validate and ML model
- Overfitting and underfitting
- Cross validation
- Validation curve
- MAPIE for confidence prediction intervals estimation
- Why You Should Never Use Cross-Validation
Model monitoring¶
- Static threshold vs anomalies and changepoints detection
- Different retrain strategies for ML models
- An end-to-end implementation of a prediction flow for kids who can't MLOps good
- Giskard: scan AI models to detect risks of biases, performance issues and errors
- MLflow
- Model drift
- Evidently for model monitoring
- Weights and Biases
- Sacred
- Omniboard as a Sacred frontend
- MLflow 101
- deepchecks
- MLNotify for training completion notification
- NannyML for post-deployment model performance monitoring
MLOps¶
- What is MLOps
- MLOps maturity checklist
- Why data makes MLOps different
- ML model deployment strategies
- MLOps lifecycles
- A curated (awesome!) list of open source libraries to deploy, monitor, version, scale and secure production machine learning
- The Full Stack 7-steps MLOps framework
- CD for ML
- Our MLOps story: Production-Grade Machine Learning for Twelve Brands
- No, You Don't Need MLOps