Amazon SageMaker JumpStart
Built-in algorithms and pre-built machine learning (ML) solutions that you can deploy with just a few clicks
Access to hundreds of built-in algorithms with pre-trained models from popular model hubs
Common use cases that can be deployed readily with just a few clicks
Fully customizable and reference architectures to accelerate your ML journey
Built-in Algorithms
SageMaker JumpStart provides hundreds of built-in algorithms with pre-trained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.
| Type | ML Tasks | Example Algorithms and Models |
| Vision | Image Classification Image Embedding Object Detection Sematic Segmentation |
200+ models including ResNet, Inception, MobileNet, SSD, Faster RCNN, YOLO |
| Text | Sentence Segmentation Text Classification Embedding Pair Classification Question Answering Summarization Text Generation Translation Named Entity Recognition |
100+ models including BERT, RoBERTa, DistilBERT, Distillbart xsum, GPT2, ELECTRA, Blazing Text, Sequence-to-sequence, Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM) |
| Tabular | Classification Regression |
LightGBM, CatBoost, XGBoost, Linear Learner, AutoGluon, TabTransformer, DeepAR, Factorization Machines, K-nearest Neighbors, Object2Vec, K-means, Random Cut Forest, IP Insights |
Pre-built Solutions
Pre-built solutions can be used for common use cases and are fully customizable.
| Use case | Solution | Get Started |
| Corporate Credit Rating Prediction | Multimodal (long text and tabular) ML for quality credit predictions | GitHub » |
| Predictive maintenance | Predictive maintenance for vehicle fleets Predictive maintenance for manufacturing |
GitHub » GitHub » |
| Computer vision | Product defect detection in images | GitHub » |
| Autonomous driving | Visual perception with active learning for autonomous vehicles | GitHub » |
| Fraud detection | Detect malicious users and transactions Fraud detection in financial transactions using deep graph library |
GitHub » GitHub » |
| Credit risk prediction | Explain credit decisions | GitHub » |
| Extract & analyze data from documents | Differential privacy for sentiment classification Document summarization, entity, and relationship extraction Handwriting recognition using Amazon SageMaker Filling in missing values in tabular records |
GitHub » GitHub » GitHub » GitHub » |
| Churn prediction | Churn prediction with text | GitHub » |
| Demand forecasting | Demand forecasting with deep learning | GitHub » |
| Personalized recommendations | Entity resolution in identity graphs with deep graph library Purchase modeling |
GitHub » GitHub » |
Customers
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Mission Automate
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“Thanks to Amazon SageMaker JumpStart, we are able to launch ML solutions within days to fulfill machine learning prediction needs faster and more reliably.”
Alex Panait, CEO – Mission Automate
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MyCase
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“Thanks to Amazon SageMaker JumpStart, we can have better starting points which makes it so that we can deploy a ML solution for our own use cases in 4-6 weeks instead of 3-4 months.”
Gus Nguyen, Software Engineer – MyCase
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pivotree
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“With Amazon SageMaker JumpStart, we can build ML applications such as automatic anomaly detection or object classification faster and launch solutions from proof of concept to production within days.”
Milos Hanzel, Platform Architect – Pivotree
