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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 1: Fundamentals of ML Model Deployment
  • Unit 2: Risk Management in Model Deployment

Unit 1: Fundamentals of ML Model Deployment​

In Unit 1, you explored…

  • What ML deployment really means: Making trained models available for real-world use and understanding how this differs from traditional software deployment.
  • Core MLOps concepts: Practices that combine ML, DevOps, and automation to ensure models can be reliably deployed, monitored, and maintained.
  • Containerisation: Using tools like Docker to package ML models and their dependencies for consistent and reproducible deployments.
  • Deployment workflows: Designing robust CI/CD pipelines, versioning models and code, and using strategies like blue/green and canary releases to manage change safely.
  • Monitoring and logging: Setting up systems to track performance, detect drift, log anomalies, and trigger alerts — ensuring models remain accurate and useful over time.
  • Model governance: Applying traceability, documentation, audit logs, and ethical safeguards to ensure responsible, reproducible, and compliant ML operations.

Unit 2: Risk Management in Model Deployment​

In Unit 2, you explored…

  • Understanding deployment risks across technical, operational, business, and ethical dimension including drift, security gaps, and regulatory exposure.
  • Evaluating deployment approaches such as batch, real-time, and edge, each with distinct risk profiles, especially in automated pipelines.
  • Applying safe transition strategies such as dark launches, A/B tests, and canary or blue/green deployments to minimise disruption and enable early issue detection.
  • Developing mitigation plans that include robust monitoring, rollback mechanisms, explainability tools, and compliance safeguards.
  • Analysing real-world scenarios to identify and address risks related to latency, security, and regulations with targeted, practical solutions.

Why monitoring matters in ML production​

  • Monitoring is not just about system uptime — it’s about model health.
  • Key metrics include:Model performance (e.g., accuracy, F1 score).
  • Prediction latency and failure rates.
  • Input data characteristics(e.g., missing values, feature distribution shifts).
  • Effective monitoring helps detect issues like model drift, silent failures, and unexpected input formats early.

Monitoring as a risk mitigation tool​

  • Deployed models face technical, operational, and business risks.
  • Data drift is a major technical risk, where real-world input data changes over time, reducing model accuracy.
  • This can lead to poor decisions, customer dissatisfaction, or regulatory issues.
  • Monitoring is the first line of defence against drift and performance degradation.

What is data drift?​

Data drift is a change in the distribution of input data over time in a production environment, which can reduce model prediction accuracy.

Action item: Monitoring and model risk poll​

Let’s do a quick pulse check on key concepts from Units 1 and 2! This poll explores monitoring practices and model risks in real-world deployments. No pressure — just choose what makes the most sense based on what you’ve learned.