“Most ML tools fail silently. This one refuses to.”

Benchmark is an AutoML system designed for one purpose:

Take messy, real-world datasets → produce valid ML results without crashing.

No assumptions.
No blind trust in user input.
No silent failures.

Most ML tools assume:

• Clean datasets
• Correct target selection
• Proper task type (classification/regression)

Reality:

Result → Unreliable models + confused users

Fail early. Fail clearly. Never lie.

        ┌────────────────────┐
        │   User Upload CSV  │
        └─────────┬──────────┘
                  ↓
        ┌────────────────────┐
        │ Target Detection   │
        │ (Type Inference)   │
        └─────────┬──────────┘
                  ↓
        ┌────────────────────┐
        │ Guardrail System   │
        │ (Validation Layer) │
        └─────────┬──────────┘
                  ↓
        ┌────────────────────┐
        │ Data Cleaning      │
        │ (X + y Processing) │
        └─────────┬──────────┘
                  ↓
        ┌────────────────────┐
        │ Model Training     │
        │ (Sklearn Models)   │
        └─────────┬──────────┘
                  ↓
        ┌────────────────────┐
        │ Results + History  │
        └────────────────────┘

This is what makes Benchmark different.

User selects wrong task?

✔ System overrides it
✔ Logs warning
✔ Prevents crash

Before training:

If failed → hard stop

Time is NOT a valid ML target directly.

We convert:

Example:

• Drop empty columns
• Drop constant columns
• Encode categorical features
• Extract datetime features:

Raw Data ↓ Validation ↓ Cleaning ↓ Feature Engineering ↓ Model Training ↓ Evaluation

• Target: Time
• Mixed formats
• 80% invalid rows

Error: Too many invalid time values in target column. Suggestion: Select a different target column.

• Next.js 14
• Tailwind CSS
• shadcn/ui

• FastAPI
• WebSockets

• scikit-learn

1. Upload CSV
2. Select target column
3. System analyzes:
   • Data type
   • Distribution
   • Validity
4. Guardrails activate
5. Data cleaned
6. Model trained
7. Results stored in history

Input:

100 rows 30 invalid target values

After Processing:

70 valid rows Model trained successfully

Most tools optimize for:

“Looks good in demo”

Benchmark optimizes for:

“Works on real data”

• No silent fixes
• No fake success
• No blind assumptions
• Always explain failure

• Auto feature engineering
• Model selection optimization
• Dataset profiling UI
• Explainable AI layer
• Dataset recommendations

benchmark/ ├── app/ ├── benchmark-api/ ├── public/ ├── dataclean/ ├── package.json └── README.md

Avik Ghosh
Building real-world AI tools that don’t break under pressure.

“Anyone can build a model.
Very few can build a system that survives bad data.”

Benchmark is built for the second category.