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.
Integrated tools streamline the model remediation process to maximize your AI application's uptime and ROI.
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
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.
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.
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.
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.
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.
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.
The history of machine learning has plenty of examples of performance bias, including many newsworthy ones. Striveworks now has an open-source tool, Valor, for understanding these different types of biases. This model evaluation service exposes performance bias by defining a subset of data through filters on the data attached to Valor objects.
Model drift is the number one reason AI programs fail to produce value. Fortunately, it doesn’t have to be this way. Remediation is proven to overcome model drift and keep AI projects performant longer. It’s the missing key to unlocking AI’s potential.
In your demo, we can detail the Striveworks approach to observability, auditability, and transparency.