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Model Monitoring & Retraining Workshop

This workshop will empower your data science team to proactively monitor, diagnose, and retrain machine learning models automatically—without the manual overhead. We’ll guide you through the process of implementing an effective, scalable MLOps pipeline for long-term model success.

The challenge

Your ML models are in production, but keeping them performing well is a constant battle.


What initially performed brilliantly in development gradually loses accuracy as the real world changes around it. Without proper monitoring and maintenance systems, these degradations often go undetected until they've already impacted business outcomes – costing you revenue, customer trust, and competitive advantage. These challenges typically manifest as:

Performance degradation
Data distributions change, causing your models to make increasingly inaccurate predictions. Every shift in user behavior or market conditions can significantly impact accuracy, leading to costly business decisions based on outdated models.

Manual maintenance
Your team spends valuable time diagnosing issues and retraining models. This cycle of detection, diagnosis, retraining, and redeployment consumes resources that could be directed toward innovation and new model development.

Limited resources
Building robust MLOps pipelines requires specialized expertise you may not have in-house. The intersection of data science and engineering skills needed for effective model operations is rare and expensive to maintain.

Our solution: end-to-end automated model maintenance


In this workshop, we'll guide you through building a complete MLOps system featuring:

  • Real-time model monitoring to catch performance issues early. Learn how to implement comprehensive monitoring that tracks not just accuracy metrics but also data drift indicators, allowing you to anticipate problems before they impact business outcomes.

  • Automated data drift detection to identify when retraining is needed. We'll cover statistical methods for detecting both feature and concept drift, with customizable sensitivity thresholds to balance responsiveness against false alarms.

  • Hands-on CI/CD pipeline development for seamless model updates. Build pipelines that automatically trigger retraining, validation, and deployment when specific conditions are met, ensuring your production environment always runs optimized models.

  • Comprehensive model registry for version tracking and governance. Implement best practices for model management that ensure full traceability, compliance, and the ability to roll back to previous versions when needed.

  • Explainability tools to understand model behavior changes. Learn techniques to interpret how and why your model outputs change over time, providing crucial insights for stakeholders and regulatory compliance.

Technologies covered

  • Prometheus/Grafana for monitoring infrastructure

  • Docker/Kubernetes for containerization and orchestration

  • Airflow/Kubeflow for workflow management

  • MLflow for experiment tracking and model registry

  • GCP Vertex AI for scalable machine learning operations.

What you'll gain

Enhanced model performance

Maintain accuracy and reliability over time. Your models will adapt to changing conditions automatically, ensuring business decisions are always based on the most accurate predictions possible. Our clients typically see a 25-40% improvement in long-term model performance stability.

Reduced operational overhead

Cut maintenance time by up to 70%. Automating the routine aspects of model maintenance frees your team from constant firefighting and allows for predictable resource allocation. Teams implementing these systems report spending 2-3x more time on new development versus maintenance.

Empowered data science team

Free your data scientists to focus on innovation. By removing the burden of manual monitoring and retraining, your team can concentrate on developing new models and features that drive business value. This shift typically leads to increased team satisfaction and retention of key talent.

Join the workshop


Take the next step in optimizing your machine learning operations. Join us for an in-depth workshop where you'll gain the knowledge and hands-on experience necessary to implement a robust, automated model monitoring and retraining system.


Register today to secure your spot, or reach out to us to book a personal workshop for your team!