Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Statsmodels: statistical modeling and econometrics in Python
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Fast and Accurate ML in 3 Lines of Code
A unified framework for machine learning with time series
A python library for user-friendly forecasting and anomaly detection on time series.
🌊 Online machine learning in Python
Lightning ⚡️ fast forecasting with statistical and econometric models.
pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
Python Streaming DataFrames for Kafka
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
MLBox is a powerful Automated Machine Learning python library.
Transpile trained scikit-learn estimators and transformers to C, C#, Go, Java, JavaScript, PHP and Ruby.
Visualize large time series data with plotly.py
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
Maze Applied Reinforcement Learning Framework
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.