Cloud services provide vast resources to help Artificial Intelligence and machine learning endeavors, but a hybrid strategy may be the best option in many circumstances. To get everything correctly, you’ll need an enterprise architecture strategy. Many firms, according to Wong, need to take a step back and consider what makes sense when it comes to dealing with sophisticated apps and significant amounts of sensitive data.
When it comes to Artificial Intelligence efforts, most companies are opting for a hybrid strategy. Many people prefer to develop in the cloud, but if they have a lot of data, they build on-premises and then run production in the cloud once they’re done. Many businesses have insisted that everything is done in the cloud. While there are some advantages to centralizing things, people are discovering that the benefit of saving money appears to have vanished.
In some circumstances, especially with Artificial Intelligence, the costs of storing data might be relatively high. So, if you’re teaching an model to recognize images, the difference can be tenfold. Therefore, a data platform must be built using an architecture-driven approach that allows a company to share data and get the most out of its advanced analytics expenditures in such a hybrid context. The goal is to create a data-driven culture based on platforms that provide data scientists and developers with agile, open environments to collaborate.
That technique begins with a minor step, such as aligning with an executive stakeholder. Then, choose a low-risk use case that will receive a lot of attention. Wong concedes that securing senior support and identifying low-hanging fruit for Artificial Intelligence use cases “is asking a lot” at many firms. This isn’t a simple task. However, seek use cases where you can quickly generate a successful story, which will help get others on board. So, something related to customer experience and customer insight for banking.