Most AI/ML projects don't fail because the model doesn't work. They fail because no one planned the project as a project. Data collection takes longer than expected. Feature engineering discoveries force a backtrack to data collection. Stakeholders expect a model in production two weeks after kickoff. A clear timeline with realistic phase durations—visible to everyone—prevents these failure modes.

This guide covers building an end-to-end AI/ML project timeline from data collection through production deployment using a Gantt chart.

Prerequisites

Before building the timeline, you need answers to the following:

Business objective and success metric. What decision does the model support? What metric—and threshold—constitutes success?
Data availability assessment. Has anyone queried the raw data sources? Do they exist, are they accessible, and what's the data quality?
Compute infrastructure. GPU availability, cloud budget, and training time constraints should be known before you estimate model development duration.
Stakeholder availability. Who approves the feature spec? Who signs off on model performance? Build their availability constraints into the timeline.

Phase Breakdown

Phase	Key Tasks	Duration	Dependencies	Owner
Problem Framing	Define ML objective, success criteria, data requirements doc	1 week	Business sign-off	Product + ML lead
Data Collection	Identify sources, extract raw data, establish data pipeline	2–4 weeks	Data requirements doc	Data engineering
Exploratory Data Analysis	Distribution analysis, missing value audit, correlation study, label review	1–2 weeks	Raw data available	Data scientist
Feature Engineering	Feature creation, encoding, normalization, train/val/test split	2–3 weeks	EDA complete	Data scientist
Model Development	Baseline → experimentation → hyperparameter tuning → final selection	3–6 weeks	Features available	ML engineer
Model Evaluation	Offline metrics, business metric translation, bias audit	1–2 weeks	Model candidate ready	ML + Product
Production Infrastructure	Serving layer, API or batch pipeline, monitoring hooks	2–3 weeks	Model artifact defined	ML Ops
Staging & Testing	Integration tests, latency validation, A/B test setup	1–2 weeks	Infra complete	QA + ML Ops
Production Deployment	Canary rollout, full traffic shift, monitoring activation	1–2 weeks	Staging passed	ML Ops

Step-by-Step Gantt Construction

Step 1: Lock the problem framing phase as a hard prerequisite

Every other row on the timeline depends on a stable problem definition. If the business objective changes after data collection starts, you may need to collect different data or re-engineer features. Add a milestone at the end of problem framing—no subsequent phase starts until it's marked complete.

Step 2: Treat EDA as its own phase with a defined output

Exploratory data analysis often reveals that the data isn't usable as-is: labels are wrong, the dataset is imbalanced, a critical feature is missing. Build EDA into the timeline as a distinct phase with a deliverable: an EDA report that either approves the data or identifies gaps that require more collection.

Step 3: Make the data collection → EDA loop explicit

Add a feedback arrow (or a conditional row) for the scenario where EDA reveals data gaps. On a Gantt chart this looks like a short "data remediation" task that feeds back into EDA. Teams that skip this end up discovering data problems during model development, which causes much larger slippage.

Step 4: Parallelize infrastructure and model development

Once the model interface (input schema, output schema) is defined—even before the final model is selected—the ML Ops team can start building serving infrastructure. Add the infra phase as a parallel track that starts two to three weeks into model development, not after it.

Step 5: Add a model card or documentation task

Before staging deployment, add a task for the model card: training data, evaluation results, intended use, known limitations. This is a production readiness requirement that often gets skipped and then causes delays when reviewers ask for it right before launch. gantt-chart.io lets you add task notes with links to documentation drafts so nothing falls through the cracks.

Common Mistakes

No EDA phase on the timeline. Teams go straight from data collection to feature engineering. When EDA reveals bad data, it's treated as an unplanned interruption rather than an expected phase. Build it in.

Model development duration underestimated. "We'll have a model in two weeks" usually means two weeks of focused work with no interruptions, no failed experiments, and no data issues. Double the estimate and add buffer.

Infrastructure as an afterthought. Serving infrastructure is often not started until the model is "done." The result is a model that's ready but can't be deployed because the API doesn't exist. Start infrastructure in parallel once the model interface is stable.

No bias or fairness evaluation. Bias audits are not optional in most production contexts. Add them as explicit tasks in the model evaluation phase with a designated owner and documented methodology.

Skipping staging latency testing. A model can have perfect accuracy and unacceptable inference latency. Measure latency in staging before it becomes a production problem.

Template Example

A 16-week end-to-end AI/ML project timeline:

Week 1:       Problem framing & business sign-off
Weeks 2–4:    Data collection & pipeline setup
Weeks 4–5:    Exploratory data analysis → EDA report
Week 5:       Data remediation (if needed, parallel with EDA review)
Weeks 5–7:    Feature engineering
Weeks 6–8:    Infrastructure setup (parallel with feature engineering)
Weeks 7–12:   Model development (baseline → tuning → selection)
Weeks 12–13:  Model evaluation & bias audit
Week 13:      Model card / documentation
Weeks 13–14:  Staging deployment & integration tests
Weeks 14–15:  A/B test setup → canary rollout
Week 16:      Full traffic shift + monitoring activation

Next Steps

Review this timeline with the data engineering lead, ML lead, and product owner before development starts. The three phases that most commonly slip are data collection (quality issues), model development (failed experiments), and infrastructure (configuration problems). Build explicit buffer into each. Once the chart is shared, use it as the source of truth in weekly syncs—teams that update their Gantt chart weekly catch slippage early enough to adjust scope before missing a deadline.