Industrialize and accelerate your MLOps with ML Works platform

Our ML Works platform is an enterprise-grade MLOps solution with automated workflows and pre-built accelerators to track model degradation, manage code workflow, and fast track model management.

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Challenges with Machine Learning Operations (MLOps)

Industry-wide, companies are struggling with AI projects that fail to make it into production due to bias in data, algorithms, or the teams managing them. According to Gartner, by the end of 2022, 85 percent of AI and ML initiatives will deliver erroneous results.

Operationalizing MLOps

Operationalizing MLOps

Enterprises worldwide are facing serious MLOps challenges due to the lack of optimal operationalized models. It’s difficult to have a reproducible and deterministically ‘correct’ result when there’s a mismatch between inference and training data. Other issues include black box input, multiple incoherent ML pipelines, manual deployment, lack of audit trails, among others

Cost overruns

Cost overruns act as
a huge hindrance

ML pipeline extracts patterns from training data to create model artifacts, however, most modeling machinery fails to see the light because of unscalable production environment. The industry still lacks guidelines on what the best ML infrastructure should look like.

O’Reilly’s ML adoption report says < 10% of surveyed companies are using automated tools for monitoring models in production.


Return on investment
is questionable

Analysis of data and model drift with automated alerts, enables continuous monitoring of production models. However, it requires a clear understanding of the data biases. The absence of processes/environments/resources to test independent data, bring in challenger models, and (re) caliber the metrics after iteration is still a challenge across the industry.

How We Help in Industrializing Machine Learning Operations?

Based on extensive experience in managing several AI customer engagements, Tredence developed ML Works to scale thousands of machine learning models, reduce outages and simplify model monitoring.

Extensive MLOps Capabilities to take on Enterprise ML Adoption Challenges

We deliver impact through our industrialized MLOps solution that provides a gateway to operationalize MLOps, including designing, productionizing, automating, and embedding the ML models ongoing business functions at scale for enterprise use.

Our key offerings for operationalizing MLOps include:

  • Build: Experimentation Tracking, Model Registry/Archive, Hyperparameter Tuning
  • Test: Model Accuracy, Model Performance, Optimal Model Selection
  • Model Deployment: CI/CD Versioning; Paas, Iaas, Container; Release Management
  • Model Monitoring: Business KPIs, Model Accuracy and Errors, Alerts and Notifications
Extensive MLOps Capabilities
Wide array of MLOps accelerators

Wide Array of MLOps Accelerators

Massive scale and unprecedented speed to help data scientists create an extensive pipeline of applications to cater to their E2E MLOps needs.

  • Data Drift Detection
  • Model Explainability
  • Bias Detection
  • Provenance Graph
  • Model Testing Framework
  • Specialized Auto ML Models

Platform & Product Partnership for Seamless Pluggability

The partnership ecosystem has given us access to robust tools, capabilities, and know-how.

  • Seamless data ingestion from any kind of data sources
  • 100% native to the world’s most leading cloud platforms
  • Certified resources, equipped to handle out-of-the-box MLOps integrations
Popular Cloud Sevices
Platform & Product Partnership for Seamless Pluggability
MLOps Managed Services

MLOps Managed Services

Proactive care and maintenance of MLOps infrastructure through ongoing and regular support for better operational efficiency.

  • 24/7 Model Management
  • Production SLA Commitment
  • Automated Incident Management
  • On-demand Data Science Teams

Key Features

Intuitive MLOps Graph

Intuitive MLOps Graph

Immersive visual workflow graph that provides end-to-end model visibility and pipeline traceability.

Active Drift Detection

Active Drift Detection

Analysis of data and model drift with automated alerts, enabling continuous monitoring of production models for accuracy and relevance.
Persona Based Insights

Persona Based Insights

Metrics customized for individual user persona to provide the most relevant information for each function.
Explainable AI

Explainable AI

Human-friendly explainable AI to equip non-technical users with a simplified explanation of model predictions.

Why Tredence?

Tredence, a leading data science and AI Engineering Company focused on solving the last mile problem in analytics, assists the world’s most prestigious brands in overcoming their most pressing machine learning operations challenges.

E2E MLOps Engagement Model

The ML Works platform commence with a POC to show value and then scale across the model universe.

E2E Governance Management

We deliver the right governance controls for delivering high-quality machine learning operations solutions and sustaining the solution in the longer term with optimized deployment strategies.

Autonomous ML Model Monitoring

Our ML Works platform helps organizations ensure their models in production are current, contextual and provide deeper visibility to data scientists for faster value realization.

Tredence aims to make ML adoption simple, pragmatic, and accessible through ML Works.

Focused Data Scientist

With ML Works, data scientists can shift their focus from managing machine learning models and mitigating risks to augmenting AI innovations.

Lean & Agile Adoption

Tredence reduces time-to-value by 30% vs. traditional consulting and technology service companies by leveraging our proprietary suite of accelerators.

  • Cloud-agnostic
  • Flexible & Scalable
  • Standardized debugging
  • AI fairness & Explainability
  • 360-degree view of model management