TL;DR
- Simple classification models: 4–8 weeks from clean data to production
- Demand forecasting or anomaly detection: 8–16 weeks
- Deep learning / computer vision / NLP: 12–24 weeks
- Data preparation is typically 40–60% of total project time: this is the biggest variable
- The fastest path: good data ready before the project starts
"How long will this take?" is one of the first questions business owners ask. The honest answer is: it depends: but not randomly. Build time is driven by a small number of well-understood factors. Once you know those, you can estimate reliably.
This guide gives you the real numbers and explains what drives variation. It also tells you the single most important thing you can do to shorten your timeline.
Timeline by Project Type
| Project Type | Clean Data | Messy Data | Budget range |
|---|---|---|---|
| Binary classification (churn, lead scoring, spam) | 4–6 weeks | 8–14 weeks | £8k–£18k |
| Regression / numerical prediction | 5–8 weeks | 10–16 weeks | £10k–£25k |
| Time-series forecasting | 8–12 weeks | 12–20 weeks | £15k–£35k |
| Anomaly / fraud detection | 8–14 weeks | 14–22 weeks | £15k–£35k |
| Recommendation engine | 10–16 weeks | 16–26 weeks | £18k–£45k |
| Computer vision / deep NLP | 14–20 weeks | 20–32 weeks | £25k–£80k+ |
Where the Time Actually Goes
Most people assume model training is the longest stage. It is usually the shortest. Here is a realistic breakdown of time allocation for a typical ML project:
Data preparation and cleaning 40–60% Feature engineering and selection 15–25% Model training and hyperparameter tuning 10–15% Evaluation and validation 5–10% Deployment and integration 10–20%The 5 Factors That Cause Projects to Take Longer
- 1. Data quality problems discovered after project start Missing values, inconsistent labelling, data format changes, or gaps in the historical record. The earlier these are discovered, the less impact on the timeline. A pre-project data audit is essential.
- 2. Unclear or changing success criteria If stakeholders disagree about what "good" looks like: which metric matters, what accuracy threshold is acceptable, what the cost of false positives vs. false negatives is: projects loop through multiple iterations.
- 3. Delayed data access or system integration Waiting for database access, API documentation, or IT security review is common and adds weeks. Resolving access and integration requirements before development starts removes a major source of delay.
- 4. Insufficient labelled data Discovering mid-project that there are only 200 labelled examples when the model needs 1,000 may require a manual labelling exercise. This is a timeline killer that a proper pre-project assessment prevents.
- 5. Scope creep during development Adding new features, additional prediction targets, or expanded integration scope mid-project is the fastest way to double the timeline. A clear, fixed scope for the initial build is essential.
How to Shorten Your Timeline
- Run a data audit before committing to a project. Know what you have, where it is, and how clean it is. This is the single highest-leverage pre-project investment.
- Define success metrics upfront. Agree on which metric(s) define success and what the minimum acceptable threshold is. This prevents scope disputes mid-project.
- Resolve system access issues before development begins. Get database credentials, API documentation, and IT sign-off in week 1, not week 4.
- Build incrementally. A working baseline model deployed in production provides value while a more sophisticated version is developed. Do not wait for perfection to ship.
- Fix scope for the first version. Phase 2 expansions are fine. Expanding scope during Phase 1 is not. Keep the initial build focused.
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