Chapter 3 HeatWave AutoML

Table of Contents

3.1 HeatWave AutoML Features

3.1.1 HeatWave AutoML Supervised Learning

3.1.2 HeatWave AutoML Ease of Use

3.1.3 HeatWave AutoML Workflow

3.1.4 Oracle AutoML

3.2 Before You Begin

3.3 Getting Started

3.4 Preparing Data

3.4.1 Labeled Data

3.4.2 Unlabeled Data

3.4.3 General Data Requirements

3.4.4 Example Data

3.4.5 Example Text Data

3.5 Training a Model

3.5.1 Advanced ML_TRAIN Options

3.6 Training Explainers

3.7 Predictions

3.7.1 Row Predictions

3.7.2 Table Predictions

3.8 Explanations

3.8.1 Row Explanations

3.8.2 Table Explanations

3.9 Forecasting

3.9.1 Training a Forecasting Model

3.9.2 Using a Forecasting Model

3.10 Anomaly Detection

3.10.1 Training an Anomaly Detection Model

3.10.2 Using an Anomaly Detection Model

3.11 Recommendations

3.11.1 Training a Recommendation Model

3.11.2 Using a Recommendation Model

3.12 HeatWave AutoML and Lakehouse

3.13 Managing Models

3.13.1 The Model Catalog

3.13.2 ONNX Model Import

3.13.3 Loading Models

3.13.4 Unloading Models

3.13.5 Viewing Models

3.13.6 Scoring Models

3.13.7 Model Explanations

3.13.8 Model Handles

3.13.9 Deleting Models

3.13.10 Sharing Models

3.14 Progress tracking

3.15 HeatWave AutoML Routines

3.15.1 ML_TRAIN

3.15.2 ML_EXPLAIN

3.15.3 ML_MODEL_IMPORT

3.15.4 ML_PREDICT_ROW

3.15.5 ML_PREDICT_TABLE

3.15.6 ML_EXPLAIN_ROW

3.15.7 ML_EXPLAIN_TABLE

3.15.8 ML_SCORE

3.15.9 ML_MODEL_LOAD

3.15.10 ML_MODEL_UNLOAD

3.15.11 Model Types

3.15.12 Model Metadata

3.15.13 Optimization and Scoring Metrics

3.16 Supported Data Types

3.17 HeatWave AutoML Error Messages

3.18 HeatWave AutoML Limitations