Model Integration - AIQ Capability Overview
Model Integration is the process of taking a trained model from the research and development phase and integrating it...
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 Integration is the process of taking a trained model from the research and development phase and integrating it with an application or other business workflow to provide value to end users.
To deliver value from models, companies must have the ability to integrate them into internal systems, products, and services. Companies need clearly defined objectives for applying machine learning to business needs, documented strategies for governing the use of models, and standardized processes and technologies to effectively serve model predictions within business workflows to deliver value to end users.
MLOps is the intersection of Machine Learning and DevOps. It's the technology and processes for developing, deploying, maintaining, and automating machine learning models to meet business needs at scale.
Model serving takes a developed machine learning model and makes it accessible for delivering predictions to support business products or services. This could include real-time streaming predictions through an API or batch predictions made offline.
Preprocessing is performed to prepare, clean, and organize data to make it suitable for training a machine learning model. Post processing is performed to analyze, transform, and visualize model outputs to further derive insights for business or end user needs.
Model Integration is the process of taking a trained model from the research and development phase and integrating it...