Learn why Two Sigma standardized on our optimization solution to scale their research efforts. Read More

SigOpt for Enterprise

SigOpt plugs into any model framework to tune any type of model to simplify and automate the model optimization process.

Built to Scale With Your Enterprise Needs

SigOpt plugs into any model framework to tune any type of model to simplify and automate the model optimization process. With just a few lines of code, you can integrate SigOpt into any pipeline or platform to tune any model for any enterprise. As a result, we have optimized models for customers across most industries. Here, we focus on a few industries where we have the most customers to provide a few deeper examples of how we scale with the enterprise.

Algo_Trading

Algorithmic Trading

Unlock new trading strategies with advanced tuning features.

Finance

Finance & Insurance

Reduce fraud and credit risk with a higher frequency of in-production model tuning.

Gov_Security

Government & Intelligence

Accelerate the rate of simulation and model development for evolving scenarios.

For_Enterprise_Purple

Enterprise Technology

Improve team productivity without sacrificing flexibility and control.

Best Practices for an Enterprise Approach to AI & ML Development and Production

 

1. Create a workflow for differentiated models

Your business analysts will use existing models for analysis and many functional business units will purchase AI-powered packaged applications (like chatbots). But your data science and machine learning teams will build proprietary models using proprietary data to solve proprietary business problems – and need solutions to support them.

2. Retain flexibility with API-enabled solutions

The market for AI & ML solutions is moving quickly, which places a premium on retaining flexibility in the way you are evolving your modeling platform. Whereas all-in-one platforms lock you into an environment, best-in-class solutions empower your team to retain this flexibility. This will put your team in a position to respond and adjust to innovation and remain at the cutting edge.

3. Divide labor between machines and experts

Experts are best when they spend their time on tasks that benefit directly from their domain expertise – and are bolstered by machines who handle tasks that do not. As it becomes increasingly challenging to scale your machine learning team, it is critical to empower each expert with the right set of solutions so they are not wasting their time on activities that are better handled by machines.

4. Optimize in development and production

Optimization is no longer the “last mile” of model development. Rather, it is a critical capability to prototype models in development, maximize the performance of models before production, and sustain their impact in production. Leading teams rely on optimization throughout the process to amplify the impact of these models on their business.

5. Analyze and reproduce any model

As your modeling scales, so does your need for gathering and analyzing data on your models to inform future model development and make it easy to reproduce any experiment. Experiment management helps your team effectively and efficiently scale their efforts, creating a virtuous cycle.

6. Scale across the full variety of models

There is a constant temptation to follow the latest modeling trend and invest in proprietary technology designed for that particular type of model. But to efficiently scale requires investing instead in solutions that empower your team across the full range of models they may need to utilize for any particular business problem.