Hyperparameter Optimization with SigOpt on AWS

Optimizing your hyperparameters the smart way

SigOpt’s hyperparameter optimization technologies enable you to boost the performance of your AI, ML, backtest, and simulation models with optimal hyperparameter, architecture, and feature transformation tuning. The highly scalable, global AWS infrastructure is an ideal solution for the robust performance requirements of machine learning workloads.

This solution is provided by SigOpt, an AWS Partner Network (APN) Partner with AWS Machine Learning Competency designation.

  • Learn
  • Launch
  • Work with an APN Partner
  • Purchase
  • Get help
  • Learn
  • Learn

    Solution highlights:

    • Improve machine learning models 100x faster with state-of-the-art Bayesian optimization
    • Reduce costs by minimizing the waste of computational and personnel resources
    • Easily integrate SigOpt with your existing machine learning tools
    • Scale the infrastructure supporting your machine learning projects up and down on-demand
    • Keep your data and models secure with black box optimization

    Key features provided by this machine learning solution:

    • Black box optimization, which enables the SigOpt team to assess the inputs and outputs of your model without needing to look at the underlying data
    • Client Libraries for Python, R, and Java
    • Open Source integrations for Spark and scikit-learn
    • Support SLAs across every pricing plan
    • Single sign-on and team-based permissions
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    With SigOpt on AWS, you can amplify your research by optimizing your model’s hyperparameters faster and more accurately than grid search, random search and manual tuning. The solution is also faster, more stable, and easier to use than open source solutions, and leverages AWS for fast deployment, scalability, and cost-effectiveness.

    Learn more about SigOpt.

    Discover value from this solution by gaining access to AWS credits to support a Proof-of-Concept (POC) and consulting offers from AWS Competency Partners that include AWS co-investments. Click below for more information.

  • Launch
  • Launch

    SigOpt is delivered through a software-as-a-service (SaaS) model that enables you to deploy quickly and pay only for what you use. In addition to a free trial, two pricing plans are available through AWS Marketplace: 15 experiments per month, and 100 experiments per month.

    When deployed from AWS Marketplace, the setup takes less than 15 minutes and the cost of SigOpt will be rolled directly into your AWS bill. Custom pricing plans are also available by contacting SigOpt.

    Discover value from this solution by gaining access to AWS credits to support a Proof-of-Concept (POC) and consulting offers from AWS Competency Partners that include AWS co-investments. Click below for more information.

  • Work with an APN Partner
  • Work with an APN Partner

    APN Partners with Machine Learning Competency designation offer consulting services to help you quickly discover value from this data lake foundation solution. Follow these links to find more about these APN Partners and their consulting offers, and to request more information or support.

    Learn more about the AWS Competency Program »

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  • Purchase
  • Purchase

    SigOpt can help any organization that needs to tune a product or service through trial and error. It is delivered through a software-as-a-service (SaaS) model that enables you to deploy quickly and pay only for what you use. Two programs are available:

    • Enterprise plans provide full access to enhanced training and tuning solutions for any model
    • Request a free trial to see if SigOpt is a good fit for your team

    SigOpt can be deployed from AWS Marketplace, rolling directly into your AWS bill, and takes less than 15 minutes to setup.

  • Get help
  • Get help

    Discover value from this solution by gaining access to AWS credits to support a Proof-of-Concept (POC) and consulting offers from AWS Competency Partners that include AWS co-investments. Click below for more information.

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    AWS Credits
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