Once upon a time, discussions of artificial intelligence (AI) were confined almost exclusively to technology professionals. Today, that paradigm has shifted, and everyone, from the business community to policymakers and everyday people, is thinking about how this technology impacts them.
However, it’s not to say AI is without its drawbacks. In particular, directional forecasting and historical analysis capabilities sometimes encounter obstacles when attempting to deliver the crucial insights needed to meet customers’ ever-changing needs. Additionally, complex operations and a lack of visibility make learning patterns and building applications a cumbersome process. These drawbacks lead to organisations being unable to deliver services quickly, which is critical in the current hyper-competitive marketplace.
To resolve this issue, the ways businesses store and process data need to evolve so they can act on new developments as they occur, be they customer intentions or supply chain issues.
Preparing data architectures for real-time AI
The journey to enabling real-time AI starts by connecting their infrastructure to multiple current events so they can monitor important moments, alerts, and outcomes.
By combining their capabilities with just-in-time computations, organisations will be able to deliver services at the exact moment they are needed.
For example, a business operating an e-commerce application can analyse which product category a user has scrolled through and what items they have purchased. Additional information, such as day, time, and location, can provide the necessary context that enables the business to deliver a personalised selection of items that customers might want.
Maintaining smooth-sailing operations
In situations where quick decisions affecting data operations are necessary, real-time AI plays a crucial role in uncovering timely insights. This enables organisations to ensure fairness and prevent unnecessary slowdowns.
One such use case in which real-time AI can be extremely beneficial is overseeing ML models’ performances and ensuring that they continue to function seamlessly. If, at any moment, a model is underperforming, it can either be re-trained or switched out for more efficient ones.
Besides that, real-time AI can also minimise bias, which might otherwise jeopardise inclusivity. A model that constantly fails fairness tests can be taken out of the production cycle. From there, organisations can rely on a rules-based system or a different model entirely that aligns with their guidelines and culture.
Simplifying data operations
Real-time AI that is enabled by integrating ML capabilities close to the data source, can eliminate cost and complexity challenges. In particular, users can immediately process event data, features, and models via a single console.
Furthermore, users can harness increased visibility to manage how data should be transformed across multiple pipelines.
Given that not all customers access the application daily, organisations do not need to create daily predictions for all their customers. For instance, if there are only 100,000 active users on a daily basis, employees can focus on this particular segment when analysing behaviours instead of the 100 million users who use the service monthly. This approach, in turn, enables organisations to minimise operational expenditures significantly.
Harnessing data without the difficulty
Bringing AI/ML closer to the data source also equips users with a set of features that support the training and production of models. In particular, feature definitions can give users the means to create test datasets and update feature stores with the latest information.
With declarative frameworks, they can transport feature and resource definitions to various parts of the infrastructure, whether it is to inspect CI/CD pipelines or create new environments for data in different regions. This flexibility, in turn, enables developers to implement best practices easily and check if their code is working as intended.
The key to effective real-time AI
In today’s digital-first world, capturing real-time developments is crucial for addressing ever-changing market trends and personalising customers’ experiences. With the appropriate infrastructure and features, organisations can harness the full potential of AI to make the best decisions and draw customers to their front doors.
The sooner organisations start building real-time AI capabilities, the easier it will be for them to catch up with and even surpass AI leaders.
