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, Stable Diffusion
Text (Natural Language Processing)
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), Bloom
Tabular  Classification
Regression 
LightGBM, CatBoost, XGBoost, Linear Learner, AutoGluon,  TabTransformer, DeepAR, Factorization Machines, K-nearest Neighbors, Object2Vec, K-means, Random Cut Forest, IP Insights
Audio  Audio Embedding  TRILL, TRILL Distilled, FRILL 

Pre-built Solutions

Pre-built solutions can be used for common use cases and are fully customizable.

Use case Solution Get Started
Credit Rating Prediction Corporate credit rating prediction using multimodal ML for quality credit predictions Graph-based credit scoring
Explain credit decisions
Documentation »
Predictive maintenance Predictive maintenance for vehicle fleets
Predictive maintenance for manufacturing
Documentation »
Computer vision Product defect detection in images
Handwriting recognition
Object detection for bird species
Documentation »
Reinforced learning
Autonomous driving with visual perception and active learning
Distributed reinforcement learning for Procgen challenge
Reinforcement learning for Battlesnake AI competitions
Documentation »
Fraud detection Detect malicious users and transactions
Fraud detection in financial transactions using deep graph library
Financial payment classification
Documentation »
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
Documentation »
Churn prediction Churn prediction for mobile
Churn prediction with text
Documentation »
Demand forecasting Demand forecasting with deep learning Documentation »
Personalized recommendations Entity resolution in identity graphs with deep graph library
Purchase modeling
Documentation »
Price optimization  Price optimization using Double Machine Learning (ML) and Prophet forecasting
Documentation »
Healthcare and life sciences analytics Lung cancer survival prediction  Documentation »

Customers

  • 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
    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
    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  

Get started with Amazon SageMaker JumpStart

Blogs

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Incremental training with Amazon SageMaker JumpStart

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Amazon SageMaker JumpStart models and algorithms available via API

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New built-in Amazon SageMaker algorithms for tabular data modeling

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Transfer learning for TensorFlow image classification models

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Detect financial transaction fraud using a Graph Neural Network with Amazon SageMaker

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Deep demand forecasting with Amazon SageMaker

Hands On Exercises

Tutorial

Step-by-step tutorial to get started with SageMaker JumpStart

WORKSHOPS

Explore how to use SageMaker JumpStart for use cases

What's new

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