Async review
Instruction and application
In ProgressRecap 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.