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Hyperparameter optimization is often considered the “last mile” of model development before a model is put into production. SigOpt customers, however, use optimization much earlier in the process to develop and deploy high-performing models at a much greater rate.
Empower Your Experts, Don’t Replace Them
Your experts are your most valuable machine learning asset, yet many AutoML solutions seek to replace them. These tools often provide optimization, but it is only available as part of a product that automatically generates models or requires you to use a specific model management solution. Any of these solutions is useful for different types of modeling needs, but standalone hyperparameter optimization is the best fit for teams of researchers building differentiated models specific to a given enterprise. And when researchers are able to build and deploy these differentiated models, companies begin to maximize the impact of modeling on their business.
The Value of Optimization Scales with Your Modeling
As teams apply optimization earlier and more frequently in the modeling process, they develop high-performing models at a faster pace. This virtuous cycle increases the number of models that make it into production, which amplifies the impact of these models on the business.
- Accelerate AI Development: Develop high-performing, differentiated models at a faster rate to amplify the impact of these models on your business
- Efficiently Scale: Maximize utilization of computing resources to scale the volume, variety and complexity of models in development and production
- Improve Productivity: Automate repeatable tasks that do not benefit from domain expertise so your experts focus their effort where it will have the greatest impact