Introduction

Introduction

Complete

Introduction

This page introduces the core goals, expectations, and practical focus for this session. Read through it before moving into the activities below.

Have you ever built a high-performing ML model, only to find that it crashes under load, racks up unexpected cloud costs, or can’t keep up with real-time demand?

Modern ML systems go far beyond models and code. They rely on powerful backends — cloud-based, hybrid, or on-prem — that can ingest data, train models, serve predictions, and auto-scale as demand fluctuates. This unit builds on your existing understanding of ML deployment by focusing on the architectural decisions and resource strategies that make ML systems not only functional but resilient,performant, andcost-effective at scale.

You’ll dive into infrastructure categories like PaaS and SaaS, explore when to use CPUs versus GPUs or TPUs, and simulate real-world trade-offs between latency, throughput, and cost. This will help you plan and optimise robust ML platforms that meet both technical and business needs.

Why does this unit matter?

Even the most advanced machine learning models can fail to deliver value if they aren’t deployed on the right infrastructure. Choosing the right architecture and allocating resources effectively can mean the difference between a scalable, cost-efficient ML system — and one that’s sluggish, expensive, or unreliable.

Beyond infrastructure, the effectiveness of an ML system also depends on the model itself — its complexity, accuracy, and latency characteristics. These factors directly influence how well a model fits the deployment environment, the resources it requires, and the value it ultimately provides. In this unit, while we focus on infrastructure, we’ll also explore how to critically evaluate the interaction between your chosen ML approach and its deployment context to achieve optimal performance and impact.

Whether you're building a recommendation engine for millions of users or deploying models across edge devices with limited resources, the skills you build here will enable you to scale responsibly, meet real-world demands, and support your organisation's operational goals.

Learning objectives

By the end of this unit, you will be able to…

• Analyse various ML/AI platform architectures and their suitability for different computational problems and operational requirements.
• Critically assess the relationship between model capacity, computational resources, and operational requirements in ML/AI systems.
• Critically evaluate the characteristics of different ML methods (e.g., model complexity, accuracy, latency) on deployment strategies, resource allocation, and overall organisational impact.
• Design and deploy scalable ML/AI platform architectures that efficiently allocate resources based on varying computational demands and operational constraints.
• Implement advanced resource allocation strategies for ML/AI workloads, optimising for performance, cost-efficiency, and adaptability to changing demands.

Before you continue, make sure you've completed:

• Module 11 Unit 2: Risk Management in Model Deployment

Action item: Pause and reflect

Before diving into the unit, take a moment to reflect.

Questions & Reflections

Have you ever encountered performance bottlenecks or unexpectedly high costs when running ML experiments or trying to serve a model? What were the challenges?

What comes to mind when thinking about making an ML system not just work but scale efficiently for a growing number of users or real-time data streams?