Amazon SageMaker JumpStart

Machine learning (ML) hub with built-in algorithms, foundation models, and prebuilt ML solutions that you can deploy with just a few clicks

Hundreds of built-in algorithms with pretrained models from popular model hubs

Popular foundation models that can be deployed with just a few clicks

Fully customizable solutions for common use cases with reference architectures to accelerate your ML journey

Share ML models and notebooks across your organization to accelerate ML model building and deployment

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment.

How it works

  • Built-in algorithms with pretrained models
  • Solutions
  • Solutions how it works diagram
  • ML artifact sharing
  • ML artifact sharing HIW diagram

Built-in algorithms

SageMaker JumpStart provides hundreds of built-in algorithms with pretrained 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 

Foundation models

Foundation models are large-scale ML models that contain billions of parameters and are pretrained on terabytes of text and image data, so you can perform a wide range of tasks such as article summarization and text, image, or video generation. Because foundation models are pretrained, they can help lower training and infrastructure costs and enable customization for your use case.

Foundation models available through SageMaker.

LightOn Logo
Stability AI Logo
Hugging Face Logo
Alexa Logo

Prebuilt solutions

Prebuilt 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 and 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

  • Tyson
  • Tyson
    Tyson
    “At Tyson Foods, we continue to look for new ways to use machine learning (ML) in our production process to improve productivity. We use image classification models to identify products from the production line that require package labels. However, the image classification models need to be retrained with new images from the field on a recurring basis. Amazon SageMaker JumpStart enables our data scientists to share ML models with support engineers so they can train ML models with new data without writing any code. This accelerates the time-to-market of ML solutions, promotes continuous improvements, and increases productivity.”

    Rahul Damineni, Specialist Data Scientist, Tyson Foods

  • 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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