We often talk about the big challenges AI and machine learning help overcome, but these systems are the result of years of research. Building and maintaining machine learning solutions requires significant development manpower and cost that many companies don’t have.
Luckily, there are other options.
While 46% of CIOs have plans to implement AI, only 4% have done so. – Gartner
Machine learning products enable solutions at scale
Machine learning products enhance organizational capabilities by creating repeatable, standardized processes, critical for scale.
Once standardized processes are in place, much of the data science work (cleaning data, preparing it for use, and building models) can be outsourced to modern AI products, so companies get a leg up on projects.
These machine learning products enable a company’s staff to spend more time integrating customized models into their own products/offerings, rather than fine-tuning hundreds of models.
Will I get a ready-to-go box of AI under the tree this year?
Not quite yet. Some level of customization is required: integration with UI, etc. But products like Darwin enable companies to speed up the model-building process from weeks to days, as done for a major E&P operator.
Transfer learning, which stores knowledge from one problem for use on the next, could help accelerate this process even further. We are actively exploring methods of this nascent technology at SparkCognition.
You don’t need a drill, you need a hole in the wall.
I always emphasize that AI is a tool, not the value. So what value does AI add?
- AI enables customization and adaptation to a changing data spectrum
- AI enables companies to be more productive and accurate, and to automate certain processes.
- AI provides the ability to be specific to each unique problem
A product that uses machine learning provides valuable personalization that a human could not. You’re not replacing a person<, you’re replacing what they can’t do at scale.