The Challenges of Current AI Architectures
There’s a big problem faced by many organizations that are trying to unlock the promise of machine learning and artificial intelligence. The process of building and using machine learning architecture doesn’t move at the speed of business. In fact, data scientists and developers are attempting to build the most powerful, sophisticated applications for the next generation of business on an infrastructure and architecture built for the demands of the last generation.
Compounding this, consumers have grown to expect that every digital experience is hyper-personalized and delivered instantaneously. This means that businesses must respond by capturing consumer opportunities the moment they happen. The businesses that have already evolved to these real-time demands are the unique few that continue to thrive, even in today’s rapidly changing marketplace.
Artificial Intelligence is the type of technology that should produce instant, real-time insights that deliver on all these consumer expectations and business needs. Machine-to-machine communication promises to extract insights at a pace far beyond the speed of humans, with a degree of accuracy that exceeds human ability and from a volume of data that would overwhelm even the largest analytics teams. But ironically, it has generally failed to do so. Here, I’ll detail the four most common challenges that stymie ML initiatives.
Demographic machine learning versus behavioral
One of the core limitations of ML/AI today is that it’s built primarily to predict individual behavior based on broad demographic data. This approach made sense at a time when serving real-time applications and real-time ML wasn’t possible and directional forecasting was considered optimal. Giving broad insights into users based on similar demographics over long periods of time was good enough to achieve KPIs or revenue requirements. However, this old approach has obvious limitations today.
Too many businesses are missing opportunities to tailor an experience or an engagement to the exact needs of an individual given their intent and context. This means missing the opportunity to adapt and prioritize an offer or an insight captured during a single session that might be more valuable than the entirety of the customer’s engagement or pattern of behaviors.
Batch processing in a real-time world
Further limiting the impact of AI is the type of data that it analyzes. The majority of AI systems were built around batch processing and historical analysis. On a nightly, weekly, or event-based schedule, data is collected from batch processing, which creates pre-aggregated tables with data from a warehouse, a file, or a bunch of other sources. This makes capturing real-time insights becomes prohibitively manual and complex.
Markets today move much more rapidly; it’s a dynamic environment that requires real-time inputs to get optimal results. Understanding data in real-time helps to keep customers engaged, but it’s also vital to preserving margins, even as market conditions remain volatile. In the end, this outdated process of bringing your data to your ML architecture costs enterprises real-time insights and opportunities to meet the demands of today’s marketplace.
Bringing data to machine learning (instead of the other way around)
The majority of ML systems are built around the basic concept of bringing data to the machine learning platform, to achieve directional forecasting. Even when real-time data is available, it’s analyzed through the same, often disconnected, process that is used to analyze historical data. This means that organizations will dedicate massive resources, time, and budget to migrate data from data warehouses and data lakes to dedicated machine learning platforms before analyzing for key insights. This is costly, and impacts how quickly we can learn new patterns and take action with customers in the moment.
Anyone engaged in building ML applications—data engineers, data scientists and developers—often work in silos with very different goals. Often wide visibility or deep understanding of a ML project is out of reach to team members spanning the data, ML, and application stack. Data models are built to serve the applications and use cases as they exist today, not the potential ML models they may serve tomorrow. Confidence is difficult to achieve when bringing data to ML models unless you know for sure that 1) the data is up to date and will be available in production and 2) you understand that the transformations took place before taking a dependency; this introduces the risk that someone along the way changed (even slightly) the pipeline definition in the production environment or will do so in the future—making the ML model predictions wrong.
Bring your AI/ML to the data
These problems are ubiquitous and create chaos for everyone involved—and frustrations for executive sponsors counting on the impact of AI initiatives. There’s a better way to deliver real-time impact and drive value by using the power of real-time A and bringing their ML to the data to deliver more intelligent applications, with more accurate AI predictions at the exact time to make the biggest business impact. To learn more about bringing ML to data, check out the DataStax Real-Time AI page.