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Async review

Instruction and application
In Progress

Recap core topics:​

  • Unit 3: ML/AI Platform Architecture and Resource Allocation

Unit 3: ML/AI Platform Architecture and Resource Allocation​

In Unit 3, you explored…

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ML/AI platform architecture basics:

The essential components that support model deployment at scale — compute, storage, model serving layers, and data pipelines.

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Operational performance metrics:

How latency (time per prediction) and throughput (predictions per second) guide deployment decisions, especially for real-time applications.

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Scaling strategies:

The difference between vertical scaling (using more powerful machines) and horizontal scaling (distributing load across many machines), and how each impacts performance and cost.

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Resource selection:

The trade-offs between CPUs, GPUs, and accelerators, and how different workloads (e.g., batch vs. real-time inference) require different hardware strategies.

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Cost-performance balancing:

How to align architectural choices with business goals by optimising for speed, scalability, and resource efficiency — ensuring models are not just accurate but also affordable to run at scale.

The performance–cost balancing act​

  • ML platforms must deliver fast predictions (low latency) andmany predictions (high throughput).
  • But higher performance often means higher cost, through more powerful machines or more infrastructure.
  • Smart deployment means balancing:Latency: How fast is each prediction?
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Throughput:

How many predictions can run at once?

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Cost:

What resources are required to support both?

Scaling strategies: Vertical vs. horizontal​

To balance these performance and cost trade-offs, teams typically scale their systems in one of two ways:

**Vertical scaling (scale up)**Upgrade to more powerful hardware (e.g., faster CPUs, more RAM, better GPUs).

  • Can improve latency for single predictions.

  • Has limits and becomes expensive quickly.

  • Best for: Low request volumes, latency-critical tasks.**Horizontal scaling (scale out)**Add more machines or instances to handle traffic in parallel.

  • Increases throughput by distributing load.

  • Often more cost-efficient at scale.

  • Requires load balancing and orchestration.

  • Best for: High request volumes, scalable cloud systems.

Action item: Scaling strategies poll​

Let’s do a quick poll to explore trade-offs in ML inference scaling. Choose the answer that you think makes the most practical sense. There’s no single right answer, just different priorities depending on the use case.

You’re launching a recommendation engine expected to receive thousands of requests per minute. What’s a more scalable strategy?
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Your finance team wants to reduce cloud costs. Which approach best balances cost-efficiency and performance for a steady, moderate traffic load?
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An ML platform must serve millions of predictions daily, but traffic varies significantly between peak and off-peak hours. What infrastructure setup best supports this need?
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In progress