Why RunML

Data Observability & Lineage

User doesn’t have observability of data and couldn’t be able to see where the data flows along the pipeline

Security & Privacy

Data is moved out of the platform and pulled into multiple systems, like Machine learning platform, Data Processing platform, Analytics platform and BI platform, Data Privacy concern arises, and data is not secure

Analytic dashboards

Too many Analytic dashboards to maintain and drill down the root cause

Data Quality

Since from the acquisition the data is moved into different pipeline due to that we see degradation of data quality and it affects the model performance degradation as well.

High Cost

Since the current MLops lifecycle has to be maintained in a different platform and retraining a model in GPU compute is going cost you without understanding the prod model in real time scenario

Benifits of RunML

Empowering DataOps

RunML is providing continuous visibility into your production Models and helping the teams to understand how models behaves in real-time and provide the lineage of data pipeline to see where you root cause of the issue arises

  • Data Observability & Lineage

    Get Observability/Lineage of Data and Model Pipelines

  • Data Quality

    Improve Data quality and Unravel data integrity issues

  • Security & Privacy

    Without moving your data build monitoring dashboards andyour data privacy and security are 100% protected

NextGen MLOps Monitoring tool

RunML provides wide range features to monitor your model at production with detecting Drift, Outlier, ExplainableAI, and Evaluation to improve your performance.

  • Model Observability/ Lineage

    Get Observability/Lineage of Model pipeline

  • Model Performance

    Get Model performance Metrics and ExplainableAI to understand how you model predictions behaves in Prod

  • Monitoring

    Detect Drift, Outlier and bias, errors and alert on conditions with wide range of integrations

Log Management

Get state-of-the-art methodologies to feature predictive alerting for log-based systems and monitor and trigger. Classify the logs and track Key Metrics which matter the most

  • Predictive Alerting & Event based trigger

    Model based predicting the logs historical data and get alerts

  • Classification of Key Metrics

    Classify logs and track the key metrics which matter the most

  • Monitoring

    Key Mtrics errors and alert on conditions with wide range of integrations

360º Assistance

Key Features

Data Quality

Detect quality of data by finding anomalies and root cause

Model Drift

Perform feature drift, concept drift, and distribution checks and monitor product model drifts

Predictive Alerting

Predict incidents based on historical data and get real-time alerts

Data Lineage

Find the origin of the issue by examining a wide range of data sources

Model Anomaly

Get outlier detection algorithms for tabular data, images, and time series

Incident-oriented Dashboard

Track the KPIs that matter
the most

Data Profiler

Profile your data with unique, null, and distinct metrics and test reports

Model Evaluation

Perform extensive analytics using calibration score, confusion matrix, segment performance, comparison, etc.

Data Integrity

Get in-depth analysis of schema, missing values, min-max and find broken properties

Model Explainability

Improve performance by understanding how the model predicts

Log Classification

Monitor key metrics by classifying and categorizing logs

KPI-driven Dashboard

Track incidents on critical KPIs to improve focus and speed-up mitigation

How RunML Enables Businesses to Enhance ML and DataOps

  • Seamless to manage MLops Life cycle and monitor the key metrics
  • Empowering Data and Model with Next generation MLOps Monitoring tool
  • Creating Analytic dashboards with Different metrics to understand the Data/model behavior
  • Connect Edge device and create monitoring dashboards on the critical metrics of the Computer vision and Sensor's system
  • Create Monitoring Pipeline for Churn prediction analysis
  • Create a Monitoring pipeline for Log-based prediction systems
  • Create a Monitoring pipeline for your ServiceNow or any Ticketing System
  • Connect, discover and monitor your MLOps lifecycle with various metrics, like Drift, Outlier, Data Quality Statistical analysis, etc.