AI/ML Model Development Project Gantt Chart

The Problem: ML Projects Fail Because the Process Is Non-Linear

Machine learning projects break traditional project management tools. Unlike software features with clear completion criteria, ML projects have feedback loops: you train a model, evaluate it, discover the data is wrong, go back to data collection, retrain, and discover a new failure mode. This non-linear process makes stakeholders impatient and makes timelines feel meaningless.

The solution isn't to abandon timeline planning—it's to plan the phases correctly. An AI/ML model development Gantt chart treats each phase as a time-boxed experiment with defined success criteria. If the model doesn't meet the accuracy threshold after three training cycles, the plan has a decision point: accept the current model, collect more data, or change approach. The Gantt chart makes these decision gates explicit.

gantt-chart.io is free and requires no account.

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Prerequisites

• Problem definition: What exactly is the model predicting or classifying? What does success look like?
• Data availability: Is training data available? How much? Is it labeled?
• Model type: Classification, regression, NLP, computer vision, recommendation?
• Infrastructure: Where will the model train? (Cloud GPU, on-prem, managed ML platform?)
• Serving requirements: Batch predictions or real-time inference? Latency requirements?
• Success metrics: Accuracy, precision/recall, F1, AUC—which metric matters for this use case?

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Step-by-Step Instructions

Step 1: Set Up the Timeline

1. Open gantt-chart.io
2. Title the chart: ML Model Development - [Model Name / Use Case]
3. Plan 12–20 weeks for a production-grade ML project
4. Add decision gate milestones after each evaluation phase
5. Use Week view

Step 2: Define the Seven ML Development Phases

1. Problem Definition & Data Assessment — scope, success criteria, data audit
2. Data Collection & Labeling — gather, clean, and label training data
3. Data Engineering — feature engineering, data pipeline, train/val/test splits
4. Baseline Model — simple model to establish baseline metrics
5. Model Development — experiment with architectures and hyperparameters
6. Evaluation & Iteration — test against success criteria, iterate
7. Production Deployment — serve model, monitor, retrain pipeline

Step 3: Problem Definition & Data Assessment (Week 1-2)

1. Define prediction target and success metric — Week 1
2. Identify all potential data sources — Week 1
3. Data availability and quality audit — Week 1-2
4. Feasibility assessment (is this solvable with available data?) — Week 2
5. Problem definition document approved — Week 2 (milestone/go-no-go)

Step 4: Data Collection & Labeling (Week 2-6)

1. Data collection pipeline built — Week 2-3
2. Raw data collected — Week 3-5
3. Labeling strategy defined — Week 3
4. Labeling tool configured — Week 3-4
5. Data labeled by domain experts — Week 4-6
6. Label quality audit — Week 6
7. Training dataset ready — Week 6 (milestone)

Step 5: Data Engineering (Week 5-8)

1. Exploratory data analysis (EDA) — Week 5-6
2. Feature engineering and selection — Week 6-7
3. Data preprocessing pipeline — Week 7
4. Train/validation/test split defined — Week 7
5. Data versioning configured (DVC or equivalent) — Week 7-8
6. Feature store or feature pipeline deployed — Week 8

Step 6: Baseline Model (Week 7-9)

1. Simple baseline model (logistic regression, rule-based) — Week 7-8
2. Baseline metrics established — Week 8
3. Baseline model decision gate — Week 9 (milestone: is baseline acceptable? If yes, ship it.)

Step 7: Model Development (Week 9-14)

1. Experiment tracking setup (MLflow, Weights & Biases) — Week 9
2. Model architecture exploration — Week 9-10
3. Training run 1: initial model — Week 10-11
4. Evaluation of training run 1 — Week 11
5. Hyperparameter tuning — Week 11-12
6. Training run 2: optimized model — Week 12-13
7. Evaluation of training run 2 — Week 13
8. Best model selected — Week 14 (milestone)

Step 8: Evaluation & Iteration (Week 14-15)

1. Evaluation against test set — Week 14
2. Error analysis (where does the model fail?) — Week 14-15
3. Bias and fairness audit — Week 15
4. Model meets success criteria? — Week 15 (decision gate)

• Yes → proceed to deployment
• No → additional data collection or architecture changes (loop back)

Step 9: Production Deployment (Week 15-18)

1. Model serving infrastructure — Week 15-16
2. API endpoint for model inference — Week 16
3. Load and latency testing — Week 16-17
4. Shadow mode deployment (predictions not served to users yet) — Week 17
5. A/B test setup — Week 17
6. Gradual rollout to production traffic — Week 18
7. Monitoring and drift detection configured — Week 18
8. Retraining pipeline scheduled — Week 18
9. Model in production — Week 18 (milestone)

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Building Decision Gates Into the Plan

ML timelines need decision gates—explicit points where you evaluate whether to continue, change course, or stop:

Gate	Question	If No
Data assessment	Is enough quality data available?	Stop or collect more data
Baseline	Is baseline acceptable for the use case?	Stop—simpler than ML
Post-training	Does model meet accuracy threshold?	Iterate or pivot
Evaluation	Does model pass fairness audit?	Fix bias before deploying

Add these as milestone tasks in your Gantt chart so stakeholders understand they're not just checkboxes—they're real decisions.

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Common Mistakes

No success criteria defined upfront. "Make the model better" is not a success criterion. Define the specific metric and threshold in Week 1, or the project never ends.

Skipping the baseline. Teams jump to complex models when a simple baseline would solve the problem adequately. A logistic regression that achieves 85% accuracy with no maintenance burden beats a neural network at 87% that requires a team to operate.

No monitoring plan. Models degrade in production as data distributions shift. A model without monitoring is a ticking time bomb. Deploy drift detection and retrain triggers alongside the model.

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