Optimization is often the difference between models that do and do not make it into production. Earn a Return on your AI & ML Investments.
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.
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.
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.