These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.
- LightGBM_Distributed_Training_Dask demonstrates the distributed training of Amazon SageMaker's implementation of LightGBM using Dask.
- Predicting Customer Churn uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives. This uses Amazon SageMaker's implementation of XGBoost to create a highly predictive model.
- Predicting Customer Churn uses Amazon SageMaker's implementation of LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular with SageMaker Automatic Model Tuning to create four predictive models on customer churn dataset, and evaluate their performance on the same test data.
- Cancer Prediction predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
- Ensembling predicts income using two Amazon SageMaker models to show the advantages in ensembling.
- Video Game Sales develops a binary prediction model for the success of video games based on review scores.
- MXNet Gluon Recommender System uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.
- Fair Linear Learner is an example of an effective way to create fair linear models with respect to sensitive features.
- Population Segmentation of US Census Data using PCA and Kmeans analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties.
- Document Embedding using Object2Vec is an example to embed a large collection of documents in a common low-dimensional space, so that the semantic distances between these documents are preserved.
- Traffic violations forecasting using DeepAR is an example to use daily traffic violation data to predict pattern and seasonality to use Amazon DeepAR alogorithm.
- Visual Inspection Automation with Pre-trained Amazon SageMaker Models is an example for fine-tuning pre-trained Amazon Sagemaker models on a target dataset.
- Create SageMaker Models Using the PyTorch Model Zoo contains an example notebook to create a SageMaker model leveraging the PyTorch Model Zoo and visualize the results.
- Deep Demand Forecasting provides an end-to-end solution for Demand Forecasting task using three state-of-the-art time series algorithms LSTNet, Prophet, and SageMaker DeepAR, which are available in GluonTS and Amazon SageMaker.
- Credit Card Fraud Detector is an example of the core of a credit card fraud detection system using SageMaker with Random Cut Forest and XGBoost.
- Fraud Detection Using Graph Neural Networks is an example to identify fraudulent transactions from transaction and user identity datasets.
- Identify key insights from textual document contains comphrensive notebooks for five natural language processing tasks Document Summarization, Text Classification, Question and Answering, Name Entity Recognition, and Semantic Relation Extracion, and zero-shot prompt engineering to solve various NLP tasks using the state-of-the-art Flan T5 XL model.
- Churn Prediction Multimodality of Text and Tabular is an example notebook to train and deploy a churn prediction model that uses state-of-the-art natural language processing model to find useful signals in text. In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields.