Identify Model Drift and Fast-Track Remediation

Real-world ROI from ML models comes from real-world performance and uptime. Striveworks is the industry-leading solution for keeping models performant in production. Our integrated toolkit streamlines monitoring, evaluation, and retraining to maximize the value of your models throughout their life cycle. 

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Support Your Post-Production ML Life Cycle

Integrated tools streamline the model remediation process to maximize your AI application's uptime and ROI.

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Monitor Production Models

Real-time, centralized monitoring detects fine-grained issues with model performance. Alert your team for immediate responses, on-platform and through native integrations.
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Evaluate and Compare Production Models

Continuous, automated testing and evaluation of models—on real production data—ensures the best model is in place for each one of your ML pipelines.
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Retrain Underperforming Models

Turn production data into a quality training set for retraining. Capture user feedback on model inferences to accelerate data cleaning and annotation processes.

Remediate and Redeploy Degraded Models Faster

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In a survey, 34% of data professionals reported data integration challenges around retraining and deployment as their #1 pain point.* With Striveworks, data teams can quickly find and use the right production data for model retraining and fine-tuning.

*Source: Thrive in the Digital Era with AI Lifecycle Synergies

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Real-Time Automated Monitoring

Real-time model monitoring and inferences

Monitor data drift, model performance, and resource consumption automatically. With Striveworks, you can go beyond basic baselines and trends and compare every incoming data point to model training data, providing a granular history for easy analysis.

  • Identify drift in unstructured data—automatically.
  • Receive real-time alerts in the platform or through integrations.
  • Explore your inference history to find patterns in anomalous data.

Learn all about model drift and ways to overcome it in our white paper, Model Drift and The Day 3 Problem.

Real-time model monitoring and inferences

Retrain and Redeploy Models Without Starting From Scratch

Add, filter, and curate production data

Get production data ready for retraining in a fast, traceable, and repeatable manner. Striveworks makes it easy to integrate and curate production data, integrate human feedback on inferences, and isolate the specific failure cohorts of models.

  • Filter and curate production data.
  • Label and clean production data on the platform for retraining.
  • Bootstrap data curation and labeling with human feedback.
  • Create new datasets, tracking every split and variation.
Add, filter, and curate production data

Reevaluate Model Performance on Production Data

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How much better is your retrained model? Know for certain with Striveworks. Our centralized evaluation store enables fine-grained evaluation of available models to ensure you promote the most suitable options for your real-world applications—systematically and consistently.

  • Evaluate model performance against production data.
  • Confirm improvements in performance post-retraining.
  • Determine the most effective model available for your data pipeline.
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Continuously Observe Comprehensive Model Metrics

Model metrics and monitoring dashboard

The Striveworks monitoring dashboard provides a clear-eyed view into performance metrics and key details for your entire model catalog. Track critical metrics that show model efficacy—and give you the knowledge to take action whenever models need attention.

  • F1 scores
  • Deployment status
  • Latency
  • Uptime
  • Total requests and requests per second
  • Data drift metrics
Model metrics and monitoring dashboard

Striveworks MLOps for the Entire Model Life Cycle

Striveworks MLOps model lifecycle diagram

The ability to create, find, and use the optimal datasets is critical across every stage of machine learning—including post-production. So, too, is the need to evaluate and observe model behavior.

Striveworks connects all critical data science workflows in an end-to-end system designed for testing and evaluating ML on real and changing data.

Build, Deploy, and Maintain Your Models With Striveworks.

Striveworks MLOps model lifecycle diagram

Remediation in Action

In 2022, a Fortune 500 customer engaged Striveworks to build a complex model using data fusion to predict lightning-induced wildfires. When weather conditions changed, incoming data naturally drifted. The Striveworks platform alerted users to model degradation and enabled immediate remediation, allowing the data science team to push an improved version live in hours—delivering 1.1 million predictions in six weeks at 87% accuracy.

Make MLOps Disappear

Discover how Striveworks streamlines building, deploying, and maintaining machine learning models—even in the most challenging environments.
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Curious About AI Governance?

In your demo, we can detail the Striveworks approach to observability, auditability, and transparency.