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People today are familiar with unexpected digital experiences - often leading to frustration with the product or service they’re trying to use.
As machine learning models are given increased responsibility for front-line interactions, it’s more important than ever for the models to better understand your customers’ intentions, so they act as expected to deliver an authentic human experience.
So, how do machine learning models calibrate to user intent?
It starts with the data.
When models break down and predictions don’t meet expectations, it’s seldom the fault of the model itself - machine learning models are only as good as the data they’re trained on.
As it’s rarely the job of a single department or group to collect, clean and manage the data, most datasets are often mis-formatted, inaccurate or incomplete.
Structurally, the most important part of a house is the foundation on which it is built. Likewise, the most critical component of a model is the data on which it is trained. Unlike concrete however, this foundation of data is ever-shifting.
Data isn’t static - its appropriateness heavily depends on ever-changing user preferences and environments.
It’s nothing without context, and context is as dynamic as the users who create it. Legacy data just can’t keep up with users’ demand for relevance.
To build a model that functions in the real world, it must be continually refreshed with data curated by humans in their specific contexts...
...but how can one possibly collect the amount of hyper-relevant data needed at the rate required to stay relevant?
At Peroptyx, we’ve assembled and pre-screened a qualified network domain-experts across the globe. We strategically match our ideal resources with each use-case requirements.
Our annotators and evaluators are bi-lingual specialists living in the same cities and countries as you. They are expert at understanding how to interact with all types of data and can provide the most valuable qualitative and quantitative feedback for your specific use-case and market.