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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 of data evaluators across the globe. We strategically match our ideal evaluators with each product to ensure authentic experiences through consistency and appropriate real-world contexts.
Our evaluators are bi-lingual specialists living in the same cities and countries as your users. 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 market.
All this means more local relevance, safer routes, better image, video and speech recognition, and the most accurate models possible.
When your models are trained and tested on relevant, human evaluated data, your users will feel understood.
Peroptyx generates deep insights into real-world user expectations through active evaluator engagement during your model training, validation and testing cycles.
We deploy advanced analytics to empower our evaluators to improve the quality of their work product - and the performance of your model - to ensure it consistently meets the expectations of your users.
For a real-world user experience that reflects real-world user expectations, ensure your machine learning model is Data Quality Authenticated™