Machine Learning Workflows & ML Models Type — ML/MLOps Pipelines DeepDive Never Forget Another ML Step: Battle-Tested Memory Techniques for the Complete Data Science Pipeline.. 📋 What You'll Master 11-Step ML Workflow: From data loading to model serving MLOps Pipeline: Production deployment & monitoring Code Structure: Clean, maintainable ML code Library-Specific: Pandas, Scikit-learn, TensorFlow, PyTorch Function Patterns: ETL, Training, Deployment, Monitoring 🎯 The Complete ML Workflow (11 Steps) Step Purpose Key Action 1. LOAD DATA Read dataset from file into memory pd.read_csv() 2. PREPROCESS Clean data, handle missing values df.dropna() 3. SPLIT DATA Separate training and testing sets train_test_split() 4. SCALE FEATURES Normalize features (mean=0, std=1) StandardScaler() 5. INITIALIZE MODEL Set up model with hyperparameters RandomForestRegressor() 6. TRAIN MODEL Learn patterns from training data mo...