Monitor Your Models, Trust Your Inferences

How do you know your machine learning models are effective in production? Track model health—status, requests, resource usage, data drift—for your entire catalog with the Striveworks MLOps platform.

View all models in production and drill down to see individual performance for both unstructured and structured data models. Then, use our integrated remediation tools to resolve problems as quickly as they arise.

 

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Comprehensive Performance Monitoring

Striveworks provides observability across every phase of your machine learning life cycle.

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Monitor Your Full Model Catalog

Real-time dashboards offer full visibility of your models in production—or still in development.
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Investigate Individual Model Performance

Drill into fine details of your models and data to pinpoint the factors affecting your model performance.
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Detect—and Defeat—Data Drift

Identify early signs of model degradation, capture production data for retraining, and start remediation immediately.

Understand Performance Across All Available Models

The Striveworks user interface showing several ML models in production

Monitor your entire catalog of models and inference servers from development through production with Striveworks. Easy-to-read dashboards let you track all models, so you can monitor deployment status, data drift, requests, uptime, and more at a glance.

Filter dashboards by:
  • Model task (object detection, image classification, named-entity recognition)
  • Framework
  • Architecture
  • Evaluation metrics
Observe crucial deployment details:
  • Data drift alerts
  • Average latency
  • CPU/GPU/RAM usage
  • Total requests 
  • Requests per second
The Striveworks user interface showing several ML models in production

Drill Down Into Individual Model Health

Model training parameters and abstract charts and graphs

When a model’s performance raises eyebrows, swiftly drill down to investigate the specifics. Explore both real-time data pipelines and model training details:

  • Failed and successful requests
  • Training hyperparameters
  • Training dataset versions
  • Recent project contributors
Model training parameters and abstract charts and graphs

Automate Drift Detection on Unstructured Data

The Striveworks user interface showing a chart that represents a model's data drift

Other platforms struggle to detect drift on data types like images and text. Striveworks is built for unstructured data first, delivering best-in-class tools that show you where and why your models are underperforming.

  • Automate drift detection for computer vision and natural language processing (NLP) models

  • Detect drift in real time or batch on a custom cadence
  • Understand drift characteristics down to individual datums with our inference store
The Striveworks user interface showing a chart that represents a model's data drift

Take Immediate Action

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Underperforming models must be fixed or retired before they impact business decisions. Striveworks makes model remediation fast and easy.

Use the best data—your production data—to retrain your models. Easily build new datasets from real-world data to quickly retrain models and to evaluate and compare performance across models.

Monitor, retrain, and evaluate—that’s remediation.

Learn more about the Striveworks approach to remediation in our white paper Model Drift and The Day 3 Problem.

Man working on a laptop fine-tuning models

Deep Dive Individual Inferences

The Striveworks user interface showing a model's inference history

The Striveworks inference store gives your team full access to your entire inference history. Examine the data and metadata of all model outputs for the most granular understanding of model performance and to identify opportunities for improvement.

  • Validate inferences and inspect data flagged as out of distribution. 
  • Search and filter inferences based on model architecture, labels, drift metrics, and other metadata.
  • Assemble and use training datasets from recent production data.
The Striveworks user interface showing a model's inference history

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