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Skills application

Skills application
Complete

ML deployment and risk mitigation plan​

In this skills application, you will apply your learning to analyse deployment risks and design a mitigation plan for an ML model being prepared for real-world deployment.

You’ll evaluate latency, security, and compliance risks in a given deployment scenario, then propose practical, targeted strategies to minimise them. This exercise will help you develop the analytical and communication skills needed to design safe, reliable, and compliant ML systems.

Context​

A financial services company is preparing to deploy a real-time credit fraud detection model. The model will run as an online inference API integrated directly into its core transaction processing system. It will evaluate thousands of transactions per second to flag high-risk activity and prevent fraud in real time.

Skills Application illustration

Key facts:

  • Model purpose: Real-time fraud detection.
  • Deployment context: Cloud-based microservices architecture, online inference via REST API.
  • Data used: Personally identifiable financial data (PII), including transaction histories, customer profiles, and geolocation data.
  • Volume and velocity: High throughput. Peak loads exceed 10,000 predictions per second.
  • Infrastructure: Deployed in a public cloud environment with autoscaling enabled, using containerised workloads.
  • Regulations: Must comply withGDPR,PCI DSS, and internal AI model governance policy requiring audit trails and explainability for high-stakes decisions.

Success criteria​

To successfully complete the skills application, you must submit a structured risk analysis and mitigation report that includes:

  • Identification of latency, security, and compliance risks based on the scenario.
  • Practical and clearly justified mitigation strategies for each risk.
  • A proposed transition strategy to minimise disruption and risk during deployment.
  • Clear reasoning and alignment with the scenario context. Completing this activity will ‘unlock’ the solution example on the following page.

Instructions and materials​

Use the form at the end of this page to structure your response. You are expected to:

Analyse latency risks​

  • Identify factors in the scenario that may lead to unacceptable prediction latency.
  • Suggest architectural or operational changes to address those risks.

Identify security vulnerabilities​

  • Highlight potential points of failure or exposure in the model’s deployment or operation.
  • Propose security measures to address these threats.

Assess compliance risks​

  • Evaluate how well the deployment aligns with GDPR, PCI DSS, and internal governance policies.
  • Identify gaps and suggest ways to close them.

Develop targeted mitigation plans​

For each category of risk (latency, security, compliance), propose concrete solutions.

Propose seamless transition strategies​

Recommend a suitable deployment rollout strategy (e.g., canary, blue/green), and explain how it supports low-risk launch and compliance assurance.

Submit your answers using the embedded form below to mark the activity as complete.

Go deeper​

After completing the activity, consider these questions:

  • What’s the biggest risk in this deployment — and how would you communicate its urgency to a nontechnical stakeholder?
  • Which mitigation would require the most cross-functional coordination to implement?
  • How might this plan change if you were deploying the model on edge devices instead of in the cloud?

Questions & Reflections​

1. Latency risk analysis and mitigation​

The risks I identified are:​

2. Security vulnerability analysis and mitigation​

The risks I identified are:​

3. Compliance risk assessment and mitigation​

The risks I identified are:​

4. Seamless transition strategy​