ML Works – Tredence’s Machine Learning Operations Platform.

Accelerate your machine learning lifecycle.

Enable Data Scientists to Focus on Innovation.
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Accelerator Synopsis

ML Works is an end to end machine learning model management accelerator enabling MLOps at scale, from model generation, orchestration, and deployment, to monitoring, health, diagnostics, governance, and business metric tracking.


It enables white-box model deployment and monitoring to ensure complete provenance review, transparency, and explainability of model. With the current business challenges and uncertainty, it has become imperative that AI/ML models driving an organization’s business decisions continue to do so effectively.

In today’s ever-changing landscape, model and data drift are both very real, and AI/ML models thus need to be monitored on a real-time basis to ensure they stay relevant. When ML models fail in live production, not only do they require valuable data scientists time to fix and redeploy them, they also disrupt the organization’s day to day operations and put it at risk of lapsing on regulatory requirements.


The ML Works accelerator provides customers with a visual provenance graph for an end to end model visibility and pipeline traceability, as well as persona-based dashboards to make real-time model monitoring easy for all personas from Data Engineers to business users. It also allows continuous monitoring of production models for accuracy and relevance, with auto-triggered alerts in the event of model and data drift.


MLOps Graph

Visual Provenance graph for an end to end model visibility and pipeline traceability, allowing easy troubleshooting of production issues and root cause analysis.

Persona-Based Dashboards

Relevant metrics for Business users, Data scientists, ML engineers, and Data engineers, with a persona-based monitoring journey to make monitoring easy for all personas.

Model & Data Drift Analysis

Analysis of data and model drift with auto-triggered alerts, enabling continuous monitoring of production models for accuracy and relevance.

Lineage Tracking

Provenance review from dashboard metrics all the way back to base models, ensuring full visibility into model operations, including training data.

Governance & Support

Centralized access control, traceability and audit logs to manage multiple user personas & ensure regulatory compliance across platforms, as well as maintenance and uptime governance through an SLA driven response and resolution process.

Platform-Agnostic Advisory & Development

Cross-platform and open source setup and support models to cater to myriad business requirements.



ML Works for the trial version supports – Regression and Classification models. Although the drift jobs are model agnostic, they run for any model type as long as the data is prepared according to ML Works input format.
In the trial version as ML Works does not have access to the code, only preprocessed files for training and testing sets are accepted (check out the documentation here) while a serialized pickle file is accepted for model object (check out the documentation here)
Model objects can be both models and pipelines if the model or pipeline implements a predict or predict_proba function that conforms to the Scikit convention. You can find the detailed documentation here.
The drift jobs are model agnostic and do not require a model object for execution.
Yes, while creating the project you have the option of selecting the type of job you wish to run – Drift only, Explainability only or Both drift & Explainability. Although this selection can be made only once for a project.