| Machine Learning | Tutorial |
 | TensorFlow PyTorch Scikit-learn Keras XGBoost LightGBM CatBoost Fastai |
 | Google Vertex AI Azure Machine Learning AWS SageMaker H2O.ai DataRobot AutoKeras |
 | Pandas NumPy Dask Featuretools |
 | MLflow Kubeflow TensorFlow Serving TorchServe BentoML |
 | SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations) Fairlearn |
 | Training & Model Development Automated Machine Learning (AutoML) Model Deployment & Serving Edge & Mobile ML Frameworks Reinforcement Learning Frameworks Data Processing & Feature Engineering Apache Flink Flink + TensorFlow/PyTorch → Deploying and serving models in real-time Flink + Kafka → Streaming data ingestion for ML pipelines Flink + Apache Beam → Unified batch and streaming ML workflows |