Model Deployment - AIQ Capability Overview
Model Deployment is the process of taking a model that has been selected, trained and integrated and deploying it to...
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 Deployment is the process of taking a model that has been selected, trained and integrated and deploying it to a production environment while meeting versioning, availability, scalability, and security requirements.
To use models in production settings, companies must have the capability to deploy and maintain them while ensuring repeatability, reliability, and quality for end users. Effective deployment strategies help reduce deployment risks which is critical when delivering machine learning solutions at scale.
A model registry is a tool that provides centralized cataloging and versioning of machine learning models and their associated metadata, such as hyperparameters and performance metrics.
MLOps is the intersection of machine learning and DevOps. It's the technology and processes for developing, deploying, maintaining, and automating machine learning models in production environments reliably and efficiently.
Retraining machine learning models is important to help reduce degradation of model performance over time in production environments.
A/B, canary, and multi-armed bandit are statistical approaches for managing releases by directing subsets of end users to different model versions. This helps evaluate model performance between versions and identify potential issues efficiently.
Model Deployment is the process of taking a model that has been selected, trained and integrated and deploying it to...