Google Cloud Professional Machine Learning Engineer Walkthrough 14 Model Training, Algo Selection, V

Video Description

Meeting Purpose Review model training and evaluation concepts for the GCP ML Associate exam. Key Takeaways - Training Methods: Vertex AI offers three paths: AutoML (no-code for rapid prototyping), Custom Training (max flexibility for advanced models), and BigQuery ML (SQL-based for analysts). - Evaluation Strategy: Use a validation set to tune model variants and a final, held-out test set for an unbiased performance estimate on new data. - Metric Selection: Choose metrics based on the problem and business cost: Precision (false positives are costly), Recall (false negatives are costly), or RMSE (large errors are costly). - Vertex AI Tools: The platform automates evaluation, providing a confusion matrix, ROC curves, and feature importance to diagnose model behavior. Topics Model Training Fundamentals - General Training Pattern: 1. Initialize: Set model parameters. 2. Forward Pass: Generate predictions from training examples. 3. Calculate Loss: Measure error between predictions and actuals. 4. Backward Pass: Compute gradients to guide parameter adjustments. 5. Update Parameters: Adjust parameters to reduce loss. 6. Repeat: Iterate through data until convergence. Algorithm Selection by Data Type - The choice of algorithm depends on the problem, data, and constraints. - Tabular Data: - Start with tree-based methods before deep learning. - Often outperform neural networks on this data type. - Images: - CNNs are standard. - Use transfer learning with pre-trained models (ResNet, EfficientNet) for faster development. - Text: - Transformer-based models (BERT, T5) dominate NLP. - For simpler tasks, use logistic regression with TF-IDF features. - Time Series: - LSTM or GRU networks for complex patterns. - ARIMA for simpler forecasting. - AutoML (No-Code/Low-Code): - For standard problem types. - User uploads data and specifies the target column. - Vertex AI automates preprocessing, algorithm selection, hyperparameter tuning, and evaluation. - Ideal for rapid prototyping or teams without deep ML expertise. - Custom Training: - For maximum flexibility with sophisticated algorithms or specialized architectures. - User packages training code (Python with TensorFlow, PyTorch, scikit-learn, XGBoost). - User specifies compute requirements (machine types, GPUs, TPUs). - Vertex AI handles infrastructure provisioning and management. - Jobs submitted via Cloud Console, gcloud CLI, or Python SDK. - BigQuery ML (SQL-Based): - For data analysts to train models directly in BigQuery using SQL. - Limited to specific algorithms: linear/logistic regression, K-means, matrix factorization, boosted trees. - Convenient for quick experimentation. - Vertex AI provides real-time monitoring in the console. - Key Metrics: - Loss Curves: Show error reduction over time. - Accuracy Metrics: Show performance improvement. - Resource Utilization: CPU, GPU, memory. - Abnormal Patterns to Watch For: - Loss not decreasing. - Accuracy plateauing early. - Out-of-memory errors. Model Evaluation - Compares model predictions against ground truth labels on unseen data to assess real-world performance. - Evaluation Strategy: - Validation Set: Used during development to tune hyperparameters and select the best model variant. - Test Set: A final, held-out set used only once to get an unbiased performance estimate for the chosen model. This is the performance reported to stakeholders. - Vertex AI Evaluation Tools: - Automatically generates metrics after training. - Confusion Matrix: For classification (shows TP, FP, TN, FN). - ROC Curves: Visualize precision-recall trade-off. - Feature Importance: Identifies key predictors. - Vertex Explanations: Provides example-based explanations. Key Evaluation Metrics - Classification Metrics: - Accuracy: % correct predictions. Misleading for imbalanced datasets. - Precision: Fraction of positive predictions that are actually positive. Critical when false positives are costly. - Recall: Fraction of actual positives that were detected. Critical when false negatives are costly. - F1 Score: Harmonic mean of precision and recall; balances both concerns. - AUC-ROC: Measures overall classification quality across thresholds. - AUC-PR: Better for imbalanced datasets than AUC-ROC. - Regression Metrics: - MAE (Mean Absolute Error): Average absolute difference. Easy to interpret (same units as target). - MSE (Mean Squared Error): Average squared difference. Heavily penalizes large errors. - RMSE (Root Mean Squared Error): Square root of MSE. Returns original units while retaining sensitivity to large errors. - R-squared: Proportion of variance explained by the model (0 = no power, 1 = perfect fit).