Lilly Tech Systems
No-Code AI Best Practices
No-code AI makes model building easy, but building good models still requires attention to data quality, proper evaluation, and realistic expectations. These best practices help you get real value from no-code AI tools.
Data Preparation
- Quality over quantity: 500 clean, well-labeled examples beat 5,000 noisy ones
- Balanced classes: If predicting churn, include roughly equal numbers of churned and retained customers
- Handle missing data: Decide whether to fill in missing values or remove incomplete rows before uploading
- Remove leakage: Do not include features that would not be available at prediction time (e.g., using "cancellation date" to predict churn)
- Consistent labeling: For image classification, ensure labels are applied consistently. When in doubt, establish labeling guidelines
Model Evaluation
Accuracy alone can be misleading. Understand these metrics:
| Metric | What It Tells You | When It Matters |
|---|---|---|
| Accuracy | % of correct predictions overall | Balanced datasets |
| Precision | When model says "yes", how often is it right? | When false positives are costly (spam detection) |
| Recall | Of all actual "yes" cases, how many did it find? | When missing positives is costly (disease detection) |
| F1 Score | Balance of precision and recall | Imbalanced datasets |
| Confusion matrix | Breakdown of correct and incorrect predictions per class | Understanding error patterns |
Deployment Strategies
- Start with human-in-the-loop: Let the model suggest, but have humans make final decisions until you trust its accuracy
- A/B testing: Run the model alongside existing processes and compare outcomes before fully switching over
- Monitor predictions: Track model performance over time. Data drift can cause accuracy to degrade
- Retrain regularly: As new data accumulates, retrain the model to keep it current
- Plan for failure: What happens when the model is wrong? Have a fallback process
When to Move Beyond No-Code
No-code AI is the right choice for many problems, but consider moving to code-based ML when:
- Custom architectures needed: Your problem requires specialized model architectures not available in no-code tools
- Complex preprocessing: You need custom data transformations, augmentation, or feature engineering
- Scale issues: Dataset is too large for the platform, or you need real-time inference at high throughput
- Integration depth: You need tight integration with existing ML pipelines and infrastructure
- Cost at scale: At high volume, custom deployment can be significantly cheaper than platform fees
Common Pitfalls
- Overfitting to training data: A model that is 99% accurate on training data but fails on new data is useless. Always evaluate on held-out test data
- Ignoring class imbalance: If 95% of data is one class, a model that always predicts that class gets 95% accuracy but is useless
- Data leakage: Including information in features that would not be available when making real predictions
- Too few examples: No-code AI still needs sufficient training data. Plan for at least 50-100 examples per class
- Blind trust: Always validate model predictions against domain expertise before deploying
Frequently Asked Questions
How much data do I need?
It depends on the task. For image classification, 20-50 images per class can work for simple tasks with tools like Lobe. For tabular data, 100+ rows is a minimum, but 1,000+ rows typically produces much better models. More diverse, high-quality data is always better.
Can no-code AI handle production workloads?
For moderate volumes, yes. Obviously.ai and cloud AutoML platforms provide API endpoints suitable for production use. For high-throughput applications (thousands of predictions per second), you may need to export the model and deploy it on your own infrastructure.
Which tool should I start with?
For tabular data (spreadsheets), start with Obviously.ai. For image classification, try Teachable Machine first (no setup required) and move to Lobe if you need higher quality models. For enterprise needs, evaluate Google Vertex AI AutoML or Azure AutoML.