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.

Learn more about built-in algorithms »

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
Pair Classification
Question Answering
Text Generation
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
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 »


  • Mission Automate
  • Mission Automate
    Mission Automate
    “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

  • MyCase
  • MyCase
    “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

  • pivotree
  • Pivotree
    “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  



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