| AutoGluon-Tabular | Training and (optionally) validation | High-accuracy tabular prediction achieved through automated ensembling and multi-layer stacking. | Y | N | CSV | CPU or GPU (single instance only, M5) |
| BlazingText | Train | Text classification for use cases such as sentiment analysis, spam detection, and hashtag prediction. | Y | Y | Text file (one sentence per line with space-separated tokens) | CPU or GPU (single instance only, M5) |
| CatBoost | Training and (optionally) validation | Gradient Boosting Regression Neural Network. Best used when the number of data dimensions is low, or when simple linear models perform poorly. | Y | N | CSV | CPU (single instance only) |
| DeepAR Forecasting | Train and (optionally) test | Time series data forecasting. Effective for cold start problems where historical datasets might be limited. | Y | N | JSON Lines or Parquet | CPU or GPU |
| Factorization Machines | Train and (optionally) test | Supervised algorithm for sparse datasets. Metrics include RMSE for regression and log loss for binary classification. | Y | Y | recordIO-protobuf float32 | CPU (GPU for dense data, M5) |
| Image Classification – MXNet | Train and validation, (optionally) train_lst, validation_lst, and model | Leverages MXNet for faster calculation speeds and resource utilization on GPU. | Y | Y | recordIO or image files (.jpg or .png) | GPU |
| Image Classification – TensorFlow | Training and validation | Used for image classification. Performs better on CPU compared to some alternatives. | Y | File | Image files (.jpg, .jpeg, or .png) | CPU or GPU |
| IP Insights | Train and (optionally) validation | Flagging IP addresses. | N | File | CSV | CPU or GPU |
| K-Means | Train and (optionally) test | Clustering (unsupervised learning). | N | Y | recordIO-protobuf or CSV | CPU or GPU (single GPU device on one or more instances) |
| K-Nearest-Neighbors (k-NN) | Train and (optionally) test | Text mining and facial recognition. Suitable for small datasets, requiring feature scaling. | Y | Y | recordIO-protobuf or CSV | CPU or GPU (single GPU device on one or more instances) |
| LDA | Train and (optionally) test | Text classification using statistical methods. | Y | Y | recordIO-protobuf or CSV | CPU (single instance only) |
| LightGBM | Training and (optionally) validation | Gradient boosting framework. | Y | File | CSV | CPU (single instance only) |
| Linear Learner | Train and (optionally) validation, test, or both | Regression or classification tasks. | Y | Y | recordIO-protobuf or CSV | CPU or GPU |
| Neural Topic Model | Train and (optionally) validation, test, or both | Text classification using Neural Networks. | Y | Y | recordIO-protobuf or CSV | CPU or GPU |
| Object2Vec | Train and (optionally) validation, test, or both | Analyzes images or paragraphs to provide relationships between objects. | Y | File | JSON Lines | CPU or GPU (single instance only) |
| Object Detection | Train and validation, (optionally) train_annotation, validation_annotation, and model | Locating and classifying objects within images (e.g., bounding box prediction). | Y | Y | recordIO or image files (.jpg or .png) | GPU |
| PCA | Train and (optionally) test | Dimensionality Reduction (unsupervised learning). | N | Y | recordIO-protobuf or CSV | CPU or GPU |
| Random Cut Forest | Train and (optionally) test | Outlier detection and forecasting. | N | Y | recordIO-protobuf or CSV | CPU |
| Semantic Segmentation | Train and validation, train_annotation, validation_annotation, and (optionally) label_map and model | Pixel-level image classification. Common in autonomous vehicle applications. | Y | Y | Image files | GPU (single instance only) |
| Seq2Seq Modeling | Train, validation, and vocab | Solving complex language problems such as machine translation, question answering, chatbot creation, and text summarization. | Y | File | recordIO-protobuf integer tokens (not float) | GPU (single instance only) |
| XGBoost (0.90-1, 0.90-2, 1.0-1, 1.2-1, 1.2-21) | Train and (optionally) validation | Provides parallel tree boosting. A leading machine learning library for regression, classification, and ranking problems. | Y | Y | CSV, LibSVM, or Parquet | CPU (or GPU for 1.2-1) |
