FAQ
Quick answers to common questions about Neo. Contact support: [email protected].
Quick Navigation
- Getting started – Setup guide and first steps
- Data and file handling – Formats, uploads, and storage
- Platform vs extension – Choosing your deployment mode
- Task submission – Writing effective tasks
- Technical and security – Architecture, privacy, and security
Getting Started
NEO is an autonomous ML agent that automates most stages of the pipeline, from data preparation to deployment.
Workflow:
- Describe your ML task in natural language
- Provide data (upload, URL, or cloud)
- NEO analyzes data and selects models
- Receive production-ready artifacts with documentation
Example task:
Build a customer churn prediction model using customer_data.csv.
Optimize for recall since missing churners is costly.NEO handles preprocessing, feature engineering, training, evaluation, and artifact generation automatically.
NEO supports multiple ML domains:
- Tabular ML – Classification, regression, clustering, time series
- Computer vision – Image classification, object detection, OCR
- NLP – Text classification, sentiment analysis, NER, summarization
- Audio and speech – Speech recognition, audio classification
- LLM fine-tuning – Instruction tuning, LoRA, domain adaptation
- Anomaly detection – Outlier detection, fraud detection
No. NEO is designed for all skill levels.
| Beginners | ML practitioners |
|---|---|
| Use task templates | Specify models and constraints |
| Step-by-step explanations | Access detailed reports |
| Start simple, progress | Customize deployments and evaluation |
| Describe your business goal | Modify generated code in VS Code |
The key is a clear goal, not prior ML knowledge.
| Task type | Duration |
|---|---|
| Simple tabular models | 15-30 min |
| Image classification | 30-60 min |
| Large datasets (>1GB) | 1-3 hrs |
| NLP fine-tuning | 2-6 hrs |
| Custom deep learning | 4-12 hrs |
Tip: Start with a small sample, then scale.
Data and File Handling
| Format | Use case | Platform | VS Code |
|---|---|---|---|
| CSV | Tabular and time series | Yes | Yes |
| Parquet | Large datasets | Yes | Yes |
| JSON | Structured and log data | Yes | Yes |
| Images | Computer vision tasks | Yes (50MB) | Yes |
| Audio | Speech and music | Yes (50MB) | Yes |
Approach:
- Platform – Use cloud storage (S3, Google Cloud Storage, Azure Blob Storage)
- Convert to Parquet – Faster processing
- Test first – Use 10% sample
File limits:
- Platform upload: 50MB per file
- Platform cloud storage: unlimited
- VS Code: unlimited local files
Yes. Automatic detection and imputation:
| Data type | Strategy |
|---|---|
| Numerical | Mean, median, predictive |
| Categorical | Mode, “Unknown” |
| Time series | Forward fill, interpolation |
| Advanced | ML-based imputation |
Platform Mode vs VS Code Extension
| Feature | Platform | VS Code extension |
|---|---|---|
| Access | Browser | VS Code editor |
| Setup | Quick, no install | Install once |
| Data | Upload ≤50MB or cloud | Local and cloud |
| Artifacts | Downloadable | Generated in workspace |
| Code editing | View only | Full IDE and Git |
| Best for | Prototyping, collaboration | Customization, local dev, large datasets |
Platform mode: Quick results, no setup, collaborative testing
VS Code extension: Edit code, work with large local files, full IDE features, version control
Task Submission
Include:
- Goal – What to predict or classify
- Data – Files, size, key columns
- Metrics – How to measure success
- Context – Business relevance
Example comparison:
| Good | Poor |
|---|---|
| Predict customer churn using customer_data.csv (50k rows). Optimize for precision-recall balance. | Do some ML with my data |
| Task | Metric |
|---|---|
| Regression | RMSE, MAE, R² |
| Classification | Accuracy, F1, AUC-ROC |
| Time series | MAPE, SMAPE, directional accuracy |
| Ranking | NDCG, MAP, precision@k |
Map metrics to business goals:
- Minimize false positives → precision
- Catch all fraud → recall
- Balance speed and accuracy → F1-score
Technical and Security
| Feature | Description |
|---|---|
| Data encryption | At rest and in transit |
| No sharing | Never shared with third parties |
| Complete control | Delete your account from website Settings |
Platform: Cloud encrypted, no sharing. Delete your account from Settings on the NEO website — see How to delete your Neo account.
VS Code: Local files never leave your machine. Cloud access uses your credentials, and you stay in control.
Delete it from Settings on the NEO website . Neo waits 30 days. The next time you sign in, a popup asks what to do: Cancel deletion keeps the account and signs you in; Keep deletion signs you out and leaves the schedule in place.
There is no support-ticket path. Full steps: How to delete your Neo account.
Yes. NEO provides:
- Preprocessing and modeling code
- Step-by-step notebooks
- Deployment scripts
- Documentation and methodology
Improvement steps:
- Improve data quality and features
- Adjust metrics and constraints
- Provide domain knowledge
- Request specific approaches (ensembles, deep learning)
Focus on practical business impact, not perfect accuracy.
Need More Help?
- Documentation – Full docs
- Use cases – Real-world examples
- Contact support – Direct help