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Introduction

The potential for machine learning systems, such as those based on deep learning, depends on organizations having the skill to develop their models and fine tune the multitude of model configuration parameters (known as hyperparameters).

Highlights

  • SigOpt optimization frees up data scientist time to accelerate the model development process, increasing the number of models that end up in production.

Features and Benefits

  • Learn how SigOpt’s ensemble of Bayesian and global optimization algorithms reduces computing time over traditional optimization methods by using an adaptive learning approach for hyperparameter tuning.
  • Assesses how users can focus their investment on data modeling and leave SigOpt to optimize the AI models without needing to be expert in deep learning.

Key questions answered

  • What is the relation between SigOpt and the data and model used to develop the AI system?
  • Can SigOpt be used on AI systems in production to ensure they do not drift?

Table of contents

Summary

  • Catalyst
  • Key messages
  • Ovum view

Recommendations for enterprises

  • Why put SigOpt on your radar?

Highlights

  • Background
  • Current position

Data sheet

  • Key facts

Appendix

  • On the Radar
  • Authors

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