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Resource allocation for ML initiatives

Instruction and application
Complete

ML projects can be exciting - and resource-hungry. From training to production operations, you need realistic estimates for compute, storage, people, tools and ongoing maintenance. This section covers estimation, portfolio trade-offs and coordination when multiple ML initiatives compete for the same capacity.

Microscope illustration

Estimating and planning resource needs​

Many ML setbacks come from underestimating the path from prototype to production - and staying there.

Estimate across:

  • Compute: GPU needs, training duration, inference latency targets.

  • Storage: Dataset size, logs, features, model artefacts and retention.

  • People and tools: Data engineering, training, deployment, on-call; cloud and library stack.Ongoing needs:

  • Monitoring and logging.

  • Retraining cadence (scheduled or drift-triggered).

  • Infrastructure maintenance and security patching.

Example: Real-time loan risk model​

A regional bank scopes resources up front:

  • Training: Several years of history; roughly three weeks of GPU time for training and tuning cycles.
  • Deployment: Integration into loan approval;under 500 ms latency per prediction.
  • Maintenance: Quarterly retrain; about 10 engineer-hours per cycle; extra compute headroom for data growth.

Early estimates support a dedicated cloud budget, IT coordination and credible timelines for leadership.

Managing resources across ML initiatives​

Organisations rarely run one ML project in isolation.

Prioritise and balance

  • Impact vs effort: Feasibility-impact views for ROI vs complexity.

  • Reuse: Shared pipelines, feature stores or deployment platforms where sensible.

  • Short vs long term: Quick wins (e.g. lead scoring) vs strategic bets (e.g. lifetime value).Coordinate

  • Shared visibility (dashboards, joint stand-ups).

  • Scheduling heavy jobs to avoid capacity clashes.

  • Cost tags to see which models consume most resources.

Action item: Resource planning mini quiz​

Check your understanding with the questions below.

Question 1 of 2
Which item is most often underestimated when moving from notebook to production?
  • A. The font size in slide decks
  • B. Ongoing monitoring, retraining and operational compute
  • C. The number of colours in the company logo
Correct Answer: B

Feedback: Long-term ML value depends on sustainable operations, not only the first training run.

When several teams share a GPU pool, what is a good portfolio practice?
  • A. Coordinate job scheduling and use cost or usage tagging for accountability
  • B. Let every team submit unlimited jobs without visibility
  • C. Remove monitoring to free GPU time
Correct Answer: A

Feedback: Shared capacity needs visibility and coordination to avoid spikes and surprise bills.