Machine Learning Project Project Schedule
This is a practical guide to building a project schedule for a machine learning project project — the planned dates for performing activities and reaching milestones, adapted to the realities of building and deploying a machine-learning model.
What a Project Schedule is
A project schedule is the planned dates for performing activities and reaching milestones. For the full concept and how it works in general, see Project Scheduling. On a machine learning project project it plays the same role, tuned to this kind of work.
Why it matters for a Machine Learning Project project
Machine Learning Project projects live or die on building and deploying a machine-learning model. A well-built project schedule gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how machine learning project projects drift into avoidable delay and cost.
What to include
- Activity list
- Durations and dependencies
- Critical path
- Milestones
- Baseline dates
Machine Learning Project-specific considerations
Tailor the project schedule to the risks that most often derail machine learning project projects:
- Data labelling and quality
- Model drift
- Productionisation and monitoring
Example
On a real machine learning project project, the project schedule would be shaped by building and deploying a machine-learning model. In particular, it should explicitly account for the project’s biggest risks — data labelling and quality, model drift, productionisation and monitoring — rather than treating them as afterthoughts.