Model Monitoring - AIQ Capability Overview
Model monitoring represents the activities necessary to ensure models are performing to specification and improving.
for the health of people
for the health of planet
for the health of business
FOR THE HEALTH OF PEOPLE: EQUITY
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“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.”
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DATA STRATEGY
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A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery.
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PREDICTIVE ANALYTICS
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Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up.
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MACHINE VISION
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Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring.
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INTELLIGENT AUTOMATION
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This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions.
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Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. |
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Model monitoring represents the activities necessary to ensure models are performing to specification and improving.
Live models often have problems if they aren't monitored on a regular basis resulting in a poor experience for end users. This includes on-going evaluation of the model against ground truth, analyzing model exhaust, and retraining models to improve performance, reduce drift and avoid bias.
Drift means a degradation in model performance as data distributions change over time.
Model error analysis is performed to explore, understand, and isolate errors in model predictions, as well as the sources of those errors. This allows data scientists and data engineers to diagnose root causes and take corrective action.
Model monitoring represents the activities necessary to ensure models are performing to specification and improving.