Our AI Impact

 for the health of people

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 Our AI Impact

 for the health of planet

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 Our AI Impact

 for the health of business

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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.” 
Read the Case Study ⇢ 

 
    ⇲ Plan & Architect
    ⇲ Design & Build
    ⇲ Implement & Scale
    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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      ⇲ Understand
      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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            ⇲ Learn
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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. 
            Start Chapter 1 Now ⇢ 

             
              ⇲ Artificial Intelligence Quotient
              How Should My Company Prioritize AIQ™ Capabilities?

               

                 

                 

                 

                Start With Your AIQ Score
                  Model Monitoring AIQ

                   

                  Model Monitoring

                  Model monitoring represents the activities necessary to ensure models are performing to specification and improving.

                  Why does Model Monitoring matter?

                  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.

                  Complete the AIQ assessment

                  Machine Learning Monitoring

                  Actively monitoring machine learning models in production environments to ensure high reliability and quality for end users.

                  My organization is able to identify model issues in production such as drift.

                  Drift means a degradation in model performance as data distributions change over time.

                  My organization uses monitoring feedback to support continuous improvement of model performance.

                  Data Monitoring

                  Actively monitoring the data that supports machine learning models in production environments to ensure high reliability and quality for end users.

                  My organization performs error analysis to identify common types of model errors.

                  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.

                  Site Reliability Engineering

                  Strategies and processes in place to manage machine learning models and mitigate potential issues in production environments across the model lifecycle.

                  My organization monitors realtime KPIs for model performance such as latency and uptime.
                  My organization uses integrated monitoring across the full machine learning stack including models, data, pipelines, and infrastructure.

                  Monitoring Effectiveness

                  Having clearly defined roles and strategies defined and documented for monitoring machine learning models in production environments.

                  My organization manages a comprehensive machine learning monitoring framework including logging, alerts, dashboards, and visualizations.
                  My organization has clearly defined roles and responsibilities for monitoring production machine learning models.
                  My organization has defined and documented expectations for model performance.

                  Learn more about Model Monitoring

                  Model Monitoring - AIQ Capability Overview

                  Model monitoring represents the activities necessary to ensure models are performing to specification and improving.

                  Complete the AIQ assessment