Despite the fact that an increasing number of companies are rushing to implement optimization algorithms, they are discovering that developing such solutions in-house is extremely difficult. As a result, 60 % of businesses (Deloitte, State of AI in the Enterprise, 2nd Edition) prefer to deploy vendor-supplied artificial intelligence solutions. Even if it has already been decided to use vendor-supplied algorithms, the selection of a partner is critical. However, as more customizable and configurable solutions have become available, making a purchasing decision has become more difficult.
Using off-the-shelf solvers has a number of advantages and benefits, including lower costs, a faster time to market, less development time, and the elimination of the need to build an in-house data science team. Because of the wide range of off-the-shelf options available, businesses can quickly and easily find a relevant solution.
These solvers, on the other hand, have a number of flaws. While off-the-shelf software is intended to be a one-size-fits-all solution, this is rarely the case in practice. People and companies have unique requirements, and a one-size-fits-all approach does not always suffice.
In most cases, optimizing a business necessitates a more customized and tailored approach. That isn't to say that use-case-specific optimization isn't effective. Commercial solvers, for example, can help businesses succeed. The problem arises when a company wants to expand its goals from a single use case to the entire organization or portfolio of customers.
As a result, customization differs from off-the-shelf solvers. New to the market is the concept of "customization," which is a new way of looking at mathematical optimization. The technology, however, can still be tailored to specific use cases because it is highly customizable and universally applicable. So, how is this possible?
As a result of the platform's pre-integrated machine learning capabilities, such as automatic algorithm finetuning, the customizable approach to mathematical optimization is possible. Developers can configure mathematical models and solvers on a platform that has all of this technology built in. The Solvice platform incorporates all necessary technology to ensure that it is robust and stable enough to perform in production with a wide variety of data and requirements. Additionally, the Solvice platform includes documentation and user interfaces designed for a broad range of stakeholders, not just developers. Additionally, it includes characteristics such as explainability, confidence, monitoring, and feedback.
Solver deployment can be customized so that enterprises can reap the benefits of off-the-shelf solutions while avoiding the drawbacks. Yet another benefit of adopting this approach is that it helps companies achieve optimal operations using only one platform. Businesses can scale up and expand their innovation goals by adding new, specific use cases for different business processes, while still only requiring a single platform to transform an entire organization, thanks to the customizable mathematical optimization approach by Solvice.
Algorithms can now be easily leveraged thanks to this customizable approach. It enables small and medium-sized businesses to compete directly with the world's largest corporations as well as in the race for innovation and market leadership.
Link:=https://www2.deloitte.com/content/dam/insights/us/articles/4780_State-of-AI-in-the-enterprise/DI_State-of-AI-in-the-enterprise-2nd-ed.pdf
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