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GCP Tools for Handling Imbalanced Datasets in ML

1. AI Platform (Vertex AI)

• Custom Model Training: Vertex AI supports training with custom code, enabling you to implement techniques like oversampling, undersampling, or class weighting.
• Hyperparameter Tuning: You can tune parameters such as class weights using hyperparameter tuning jobs to address class imbalances.
• AutoML: AutoML tools automatically handle imbalances using internal strategies such as oversampling or class weighting.

2. BigQuery ML

• Model Training: Build and train models within BigQuery using SQL queries and apply class weighting or data sampling during training.
• Data Preprocessing: You can preprocess your data with SQL queries to handle imbalance, like implementing oversampling or undersampling.

3. Dataflow

• Data Transformation Pipelines: Create custom data processing pipelines to reshape the data by adding synthetic data or balancing classes.
• Integration with Apache Beam: Leverage Apache Beam to implement custom solutions such as SMOTE, resampling, or balancing techniques.

4. TensorFlow on GCP (AI Platform Notebooks or Deep Learning VM)

• Custom Imbalanced Techniques: Use TensorFlow’s class_weight parameter to weigh classes differently during training.
• Imbalanced-learn Library: Use the imbalanced-learn library for techniques like SMOTE, oversampling, or undersampling.
• Keras Tuner: Tune hyperparameters using Keras Tuner to improve handling of class imbalances.

5. Cloud Dataprep

• Explore and clean your dataset by filtering or duplicating rows to manually balance class distributions and handle missing data.

6. TPU (Tensor Processing Units) and GPUs

• Use TPUs or GPUs for deep learning models with techniques such as class-weight balancing or data augmentation, particularly for image datasets.

Techniques for Handling Imbalanced Data

• Resampling Techniques: Implement oversampling or undersampling using Dataflow or Vertex AI Notebooks.
• Class Weighting: Set class weights in services like BigQuery ML, AutoML, and Vertex AI to assign higher penalties to the minority class during training.

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