Tracking ML experiment result (Useful for DL project)
Weights & Biases - Developer tools for ML
Weights & Biases - Developer tools for ML Experiment tracking, hyperparameter optimization, model and dataset…
GPU version of Pandas
NOTE: For the latest stable README.md ensure you are on the main branch. Built based on the Apache Arrow columnar…
Check the dataset before you dive into it. (Useful for ML project)
Documentation | Slack | Stack Overflow Generates profile reports from a pandas DataFrame. The pandas df.describe()…
Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data…
Very similar to metaflow
The prefect Python library includes everything you need to design, build, test, and run powerful data applications…
MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since…
- Tracking experiments to record and compare parameters and results (MLflow Tracking).
- Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).
- Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
- Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).
Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production.
Would you prefer a lighter-weight, pip-install, no-Kubernetes deployment of Feast? The Feast maintainers are currently…
An open source platform to deploy your machine learning models on Kubernetes at massive scale. Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.