Some useful Data Science tool I learned from Full Stack Deep Learning

wandb

Tracking ML experiment result (Useful for DL project)

Weights & Biases - Developer tools for ML

cudf

GPU version of Pandas

rapidsai/cudf

pandas-profiling

Check the dataset before you dive into it. (Useful for ML project)

pandas-profiling/pandas-profiling

metaflow

Why metaflow

Netflix/metaflow

Prefect

Very similar to metaflow

ETL with Prefect

Prefect Core

MLFlow

MLflow Documentation

• 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

Feast (Feature Store) is an operational data system for managing and serving machine learning features to models in production.

Introduction

Seldon

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.

SeldonIO/seldon-core