AutoGluon: automated machine learning, zero code required. AutoML framework by Amazon.

CatBoost: handles categorical features, less tuning needed. ML/AI development tool.

ClearML: ML ops platform, experiment tracking, orchestration, deployment. End-to-end MLOps.

DeepSpeed: Microsoft's deep learning optimization, memory efficiency, massive models. Train trillion-parameter models.

fast.ai: high-level APIs, best practices built-in, education-focused. ML/AI development tool.

H2O.ai: enterprise AutoML, model interpretability, production deployment. Scalable ML platform.

Horovod: distributed deep learning training, multi-GPU, framework agnostic. Uber's training framework.

Hugging Face: transformers, pre-trained models, model hub. ML/AI development tool.

Keras: high-level neural networks, TensorFlow backend, beginner-friendly. ML/AI development tool.

Kubeflow: ML on Kubernetes, portable pipelines, scalable deployments. Cloud-native ML platform.

LightGBM: memory efficient, faster training, large datasets. ML/AI development tool.

Metaflow: Netflix's ML infrastructure, production-ready pipelines, versioning. Build and deploy ML.

MLflow: experiment tracking, model registry, deployment. ML/AI development tool.

ONNX: model interoperability, framework conversion, deployment flexibility. Open Neural Network Exchange.

Optuna: automated hyperparameter optimization, pruning, distributed tuning. Smart model tuning.

Prophet: Facebook's time series forecasting, handles seasonality, missing data. Automatic forecasting.

PyCaret: low-code machine learning, rapid prototyping, automated workflows. Simple ML automation.

PyTorch machine learning framework review: research, dynamic graphs, Pythonic API. Meta's ML platform for experimentation.

RAPIDS: GPU-accelerated dataframes, 50x faster data processing. NVIDIA's data science acceleration.

Ray: distributed machine learning, parallel Python, scalable ML. Build and scale ML applications.

scikit-learn Python ML library review: classical machine learning, simple API, great docs. Best for traditional ML tasks.

sktime: time series machine learning, scikit-learn compatible. Unified time series API.

TensorFlow machine learning framework review: production deployment, mobile, serving. Google's ML platform.

Weights & Biases: experiment visualization, collaboration, hyperparameter tuning. ML/AI development tool.

XGBoost: winning Kaggle competitions, speed and performance. ML/AI development tool.