Cursor vs Jupyter for Machine Learning

When you’re developing machine learning models, your choice of development environment profoundly shapes your workflow, productivity, and code quality. The two dominant approaches represent fundamentally different philosophies: Jupyter notebooks with their interactive, exploratory paradigm, and code editors like Cursor with their structured, software engineering-first approach. Jupyter has been the default choice for ML practitioners for years, offering immediate visual feedback, inline plotting, and an experimentation-friendly interface that mirrors scientific computing traditions. Cursor, representing the new generation of AI-powered code editors, brings software engineering best practices, powerful AI assistance, and production-ready code organization to ML development. Understanding when each tool excels—and how to potentially use both—is essential for modern ML engineers navigating the spectrum from experimental research to production deployment.

Understanding the Core Paradigms

Before comparing specific features, you need to understand the fundamental differences in how these tools approach ML development.

Jupyter’s Interactive Exploration Model

Jupyter notebooks organize code into cells that can be executed independently and out of order. This cell-based execution model encourages an exploratory workflow: load some data in one cell, visualize it in another, try different preprocessing approaches in subsequent cells, and iterate rapidly.

Results appear inline immediately below the cell that generated them—dataframes render as formatted tables, plots appear as images, and print statements show right where you’d expect. This immediate visual feedback creates a tight iteration loop that’s particularly valuable during exploration and experimentation phases.

The notebook itself becomes a narrative document combining code, results, visualizations, and markdown explanations. This makes notebooks excellent for communicating findings, creating tutorials, or documenting experimental processes. A well-crafted notebook tells a story from data loading through analysis to conclusions.

However, this flexibility comes with challenges. Out-of-order execution can create hidden dependencies where cells depend on state from cells run earlier, making notebooks difficult to reproduce. Variable state persists across cell executions, leading to situations where re-running a notebook from top to bottom produces different results than the current state.

Cursor’s Structured Development Approach

Cursor (and code editors generally) work with traditional Python files organized into functions, classes, and modules. Code executes sequentially from top to bottom when you run the file. This enforces a more structured approach where dependencies are explicit and execution order is deterministic.

The workflow is more deliberate: you design your code structure, implement functions and classes, run the entire script to test it, iterate on the implementation. While you can still prototype interactively using Python’s REPL or IPython, the primary paradigm is writing complete, runnable programs.

Cursor adds AI assistance throughout this process—intelligent code completion that understands your ML libraries, conversational AI for generating model architectures or data preprocessing pipelines, and codebase-wide understanding that helps maintain consistency across your ML project.

This approach encourages better software engineering practices: modular code, proper abstraction, testability, and version control friendliness. However, it trades Jupyter’s immediacy for structure, and the feedback loop for experimentation is longer.

Experimentation and Prototyping Workflows

The early stages of ML projects involve extensive experimentation—trying different approaches, exploring data, testing hypotheses. How do these tools support this critical phase?

Jupyter’s Experimentation Strengths

Jupyter excels at rapid experimentation. Load a dataset, display the first few rows with df.head(), and immediately see your data structure. Plot distributions with matplotlib or seaborn, and visualizations appear inline. This immediate visual feedback accelerates understanding.

When trying different feature engineering approaches, you can keep successful transformations in cells and comment out or delete unsuccessful attempts. The exploratory nature means you don’t need to design a complete program structure upfront—you can evolve your analysis organically as you discover patterns in the data.

For hyperparameter tuning, you can quickly modify values in a cell, re-run just that cell and subsequent cells, and see new results without rerunning earlier preprocessing steps. This selective re-execution saves time during iterative optimization.

The ability to display rich media—images, interactive plots with Plotly, even video or audio—makes Jupyter powerful for domains like computer vision or signal processing where visualizing intermediate results is crucial.

Cursor’s Structured Prototyping

Cursor’s approach to experimentation is more disciplined. You might create a experiments.py file with functions for different approaches:

def experiment_random_forest(X_train, y_train, X_test, y_test):
    """Try Random Forest with various hyperparameters"""
    model = RandomForestClassifier(n_estimators=100, max_depth=10)
    model.fit(X_train, y_train)
    accuracy = model.score(X_test, y_test)
    return model, accuracy

def experiment_gradient_boosting(X_train, y_train, X_test, y_test):
    """Try Gradient Boosting"""
    model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
    model.fit(X_train, y_train)
    accuracy = model.score(X_test, y_test)
    return model, accuracy

def experiment_random_forest(X_train, y_train, X_test, y_test):
    """Try Random Forest with various hyperparameters"""
    model = RandomForestClassifier(n_estimators=100, max_depth=10)
    model.fit(X_train, y_train)
    accuracy = model.score(X_test, y_test)
    return model, accuracy

def experiment_gradient_boosting(X_train, y_train, X_test, y_test):
    """Try Gradient Boosting"""
    model = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
    model.fit(X_train, y_train)
    accuracy = model.score(X_test, y_test)
    return model, accuracy

With Cursor’s AI assistance, you can quickly generate these experiment functions by describing what you want: “create a function to train a random forest model with cross-validation.” Cursor generates complete, properly structured code.

The structured approach has advantages: experiments are reproducible functions you can call multiple times with different data. They’re easier to version control and compare systematically. However, the feedback loop is longer—you need to run the entire script to see results rather than just re-executing a cell.

For visualization during prototyping, you can save plots to files or use tools like matplotlib’s interactive backend, but it’s not as seamless as Jupyter’s inline rendering.

The Hybrid Approach

Many ML practitioners use both: Jupyter for initial exploration and hypothesis testing, then migrate successful experiments to structured code in Cursor as they refine and productionize their approach. This hybrid workflow combines Jupyter’s exploratory power with Cursor’s structure.

Model Development and Training

Once you move from exploration to building actual models, the tools show different strengths.

Training Loops in Jupyter

Jupyter notebooks commonly contain training loops directly in cells. You can see training progress with print statements or progress bars that update in real-time within the notebook. Loss curves can be plotted and displayed inline as training progresses, providing immediate feedback on whether training is converging.

For frameworks like TensorFlow or PyTorch, Jupyter’s interactive nature is convenient—you can define a model in one cell, inspect its architecture in another, train it in a third, and evaluate it in a fourth. Each step is visible and checkpointed.

The challenge comes with long-running training jobs. If your kernel disconnects or crashes mid-training, you lose progress unless you’ve implemented checkpointing. Training on remote servers or GPUs requires additional setup like SSH tunnels or services like JupyterHub.

Debugging training issues in notebooks can be frustrating. When training fails, you might need to restart the kernel and re-run many cells to get back to the failure point. The non-linear execution history makes it hard to reproduce the exact state that caused an error.

Cursor’s Approach to Training

In Cursor, training code lives in proper Python scripts with clear structure:

# train.py
import torch
import argparse
from pathlib import Path

def train_model(config):
    # Load data
    train_loader = create_dataloader(config['data_path'], config['batch_size'])
    
    # Initialize model
    model = create_model(config['model_type'], config['num_classes'])
    optimizer = torch.optim.Adam(model.parameters(), lr=config['lr'])
    
    # Training loop
    for epoch in range(config['epochs']):
        for batch_idx, (data, target) in enumerate(train_loader):
            optimizer.zero_grad()
            output = model(data)
            loss = torch.nn.functional.cross_entropy(output, target)
            loss.backward()
            optimizer.step()
            
            if batch_idx % 100 == 0:
                print(f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item()}")
        
        # Save checkpoint
        torch.save(model.state_dict(), f"checkpoints/epoch_{epoch}.pt")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--data-path', required=True)
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--lr', type=float, default=0.001)
    args = parser.parse_args()
    
    config = vars(args)
    train_model(config)

# train.py
import torch
import argparse
from pathlib import Path

def train_model(config):
    # Load data
    train_loader = create_dataloader(config['data_path'], config['batch_size'])
    
    # Initialize model
    model = create_model(config['model_type'], config['num_classes'])
    optimizer = torch.optim.Adam(model.parameters(), lr=config['lr'])
    
    # Training loop
    for epoch in range(config['epochs']):
        for batch_idx, (data, target) in enumerate(train_loader):
            optimizer.zero_grad()
            output = model(data)
            loss = torch.nn.functional.cross_entropy(output, target)
            loss.backward()
            optimizer.step()
            
            if batch_idx % 100 == 0:
                print(f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item()}")
        
        # Save checkpoint
        torch.save(model.state_dict(), f"checkpoints/epoch_{epoch}.pt")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--data-path', required=True)
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--lr', type=float, default=0.001)
    args = parser.parse_args()
    
    config = vars(args)
    train_model(config)

This structured approach has several advantages:

• Reproducibility: Run the same script with the same arguments, get the same results
• Remote Execution: Easy to run on remote servers or cloud instances via SSH
• Background Training: Can disconnect and let training continue in the background
• Version Control: Git tracks changes clearly, unlike notebooks where diffs are messy
• Debugging: Use proper debuggers with breakpoints and step-through execution

Cursor’s AI helps you write this boilerplate quickly. Ask it to “create a training script with checkpointing and command-line arguments,” and it generates a complete, working template.

For monitoring during training, you’d typically integrate with tools like TensorBoard, Weights & Biases, or MLflow for visualization rather than inline plotting. This requires more setup but provides better long-term tracking of experiments.

Code Organization and Project Structure

As ML projects grow from single experiments to multiple models, datasets, and utilities, code organization becomes critical.

Jupyter’s Organization Challenges

Notebooks are inherently single-file artifacts. As your project grows, you might have dozens of notebooks: data_exploration.ipynb, model_v1.ipynb, model_v2.ipynb, hyperparameter_tuning.ipynb, final_model.ipynb.

Organization patterns emerge but are informal:

• Numbered prefixes: 01_data_loading.ipynb, 02_preprocessing.ipynb
• Directory structure: notebooks/exploration/, notebooks/models/
• Shared utility files: utils.py that notebooks import

Shared code between notebooks is tricky. You might copy-paste functions between notebooks (leading to duplication and inconsistency) or create .py files with shared utilities that notebooks import. The latter is better but breaks the notebook’s self-contained nature.

Functions defined in one notebook can’t easily be used in another without copying or refactoring into a shared module. This limits code reuse and often leads to duplicated preprocessing or evaluation logic across notebooks.

Cursor’s Modular Architecture

Code editors naturally support proper project structure:

ml-project/
├── data/
│   ├── raw/
│   └── processed/
├── src/
│   ├── data_loader.py
│   ├── preprocessing.py
│   ├── models/
│   │   ├── resnet.py
│   │   ├── transformer.py
│   │   └── ensemble.py
│   ├── training.py
│   ├── evaluation.py
│   └── utils.py
├── notebooks/  # For exploration only
│   └── experiments.ipynb
├── tests/
│   ├── test_preprocessing.py
│   └── test_models.py
├── configs/
│   └── training_config.yaml
└── train.py

ml-project/
├── data/
│   ├── raw/
│   └── processed/
├── src/
│   ├── data_loader.py
│   ├── preprocessing.py
│   ├── models/
│   │   ├── resnet.py
│   │   ├── transformer.py
│   │   └── ensemble.py
│   ├── training.py
│   ├── evaluation.py
│   └── utils.py
├── notebooks/  # For exploration only
│   └── experiments.ipynb
├── tests/
│   ├── test_preprocessing.py
│   └── test_models.py
├── configs/
│   └── training_config.yaml
└── train.py

Each component has a clear responsibility. Models are defined in separate files, making them easy to find and modify. Shared utilities live in modules imported throughout the project. Training and evaluation logic is centralized and consistent.

Cursor’s codebase understanding helps navigate this structure. Ask “where is the data preprocessing logic?” and Cursor searches your entire project and points you to the right file. Request changes like “add data augmentation to the training pipeline,” and Cursor understands the project structure well enough to modify the right components.

This organization supports best practices like unit testing (hard in notebooks), continuous integration, and professional development workflows. You can refactor confidently knowing the IDE will help you find and update all usages of modified functions.

Collaboration and Version Control

ML projects increasingly involve teams, making collaboration and version control essential.

Jupyter’s Collaboration Friction

Jupyter notebooks present significant challenges for collaboration:

Git Diffs: Notebooks are JSON files with metadata about execution counts, outputs, and cell state. Git diffs of notebooks are nearly unreadable—they show JSON structure changes rather than logical code changes. Reviewing pull requests with notebook changes is frustrating.

Merge Conflicts: When two people modify the same notebook, merge conflicts are nightmarish. The JSON structure makes conflicts hard to resolve manually, and notebook-specific merge tools exist but require additional setup.

Output Handling: Should you commit notebook outputs to version control? Including outputs makes diffs even larger and clutters the repository. Excluding outputs means reviewers can’t see results without running notebooks themselves.

Execution State: Notebooks shared between team members might depend on execution state—variables defined in cells that weren’t committed or that need to be run in a specific order. Reproducing a colleague’s results often requires guesswork.

Tools like nbdime, ReviewNB, and Jupytext help by providing better diffing, rendering notebooks in pull requests, or converting notebooks to plain Python scripts. However, these are workarounds for fundamental limitations.

Cursor’s Git-Friendly Workflow

Plain Python files are designed for version control. Diffs show exactly what changed—which lines were added, modified, or deleted. Merge conflicts are manageable with standard tooling. Code reviews happen in familiar pull request interfaces with clear context.

Cursor integrates Git directly into the editor, showing file status, enabling staging and committing without leaving the IDE, and displaying inline diffs. The AI can help write commit messages by analyzing your changes.

For team collaboration, having model definitions, training scripts, and utilities in properly structured Python makes it easy to work in parallel. One person can modify the data loader while another adjusts the model architecture without conflicts.

Shared code is naturally DRY (Don’t Repeat Yourself)—team members import common utilities rather than duplicating logic. Changes to shared utilities automatically propagate to all code using them.

Production Deployment and MLOps

Eventually, successful ML projects need to move to production, where the tools show stark differences.

From Jupyter to Production

Deploying notebook-based models to production requires significant refactoring. The typical path:

1. Extract model training code from notebook into a Python script
2. Remove exploratory cells and debugging code
3. Parameterize hardcoded values (file paths, hyperparameters)
4. Add proper error handling and logging
5. Create a serving interface (Flask/FastAPI endpoint or batch script)
6. Package dependencies properly
7. Containerize with Docker
8. Deploy to production infrastructure

This refactoring is substantial work, often taking days or weeks. The model that worked in the notebook might behave differently when refactored due to subtle state dependencies or hard-to-reproduce preprocessing steps.

Some teams maintain parallel codebases—notebooks for research, Python scripts for production. This creates maintenance burden and potential for divergence between research and production implementations.

Tools like Papermill help by parameterizing and executing notebooks programmatically, and services like Amazon SageMaker or Google Vertex AI attempt to bridge the notebook-to-production gap. However, these are workarounds for the fundamental impedance mismatch between notebooks and production systems.

Cursor’s Production-Ready Code

Code written in Cursor is closer to production-ready from the start:

• Already modular with clear function/class boundaries
• Easy to add command-line arguments or configuration files
• Straightforward to add logging, monitoring, and error handling
• Version controlled properly
• Can be containerized without major refactoring

You might still need to optimize, add production monitoring, or build serving infrastructure, but the core ML logic doesn’t require rewriting. The model training script you developed locally can run on cloud infrastructure with minimal modification.

For model serving, you can use frameworks like FastAPI to quickly create REST endpoints:

from fastapi import FastAPI
from pydantic import BaseModel
import torch

app = FastAPI()

# Load model once at startup
model = torch.load("model.pt")
model.eval()

class PredictionRequest(BaseModel):
    features: list[float]

@app.post("/predict")
def predict(request: PredictionRequest):
    with torch.no_grad():
        features_tensor = torch.tensor([request.features])
        prediction = model(features_tensor)
    return {"prediction": prediction.item()}

from fastapi import FastAPI
from pydantic import BaseModel
import torch

app = FastAPI()

# Load model once at startup
model = torch.load("model.pt")
model.eval()

class PredictionRequest(BaseModel):
    features: list[float]

@app.post("/predict")
def predict(request: PredictionRequest):
    with torch.no_grad():
        features_tensor = torch.tensor([request.features])
        prediction = model(features_tensor)
    return {"prediction": prediction.item()}

Cursor’s AI can generate this boilerplate, and the code integrates naturally with your existing model definition since it’s all proper Python modules.

AI Assistance and Productivity

Both Jupyter and Cursor offer AI assistance, but the integration differs significantly.

Jupyter with AI Extensions

Jupyter supports AI assistance through extensions like GitHub Copilot (via JupyterLab extension) or ChatGPT plugins. These provide inline completions within notebook cells, which is helpful for writing code.

However, the integration is limited by Jupyter’s architecture. AI assistance typically works within a single cell, not across cells. The AI can’t easily understand the full context of your notebook—what variables were defined in previous cells, what data transformations happened earlier, or what your overall analysis flow is.

For complex ML tasks spanning multiple cells, you often need to manually provide context to AI assistants, or the suggestions will be generic rather than specific to your notebook’s state and goals.

Cursor’s Deep ML Assistance

Cursor’s AI integration is more comprehensive for ML development:

Model Architecture Generation: Describe a model in natural language—”create a ResNet-18 architecture for image classification with 10 classes”—and Cursor generates the complete implementation.

Data Pipeline Building: “Create a PyTorch DataLoader for image augmentation with random flips, rotations, and color jittering” produces a working data loading pipeline.

Debugging Assistance: When your model produces NaN losses or training diverges, select the training code and ask Cursor “why might training diverge?” It analyzes your code and suggests potential issues like learning rate, batch normalization, or gradient clipping.

Refactoring Support: “Extract this training loop into a reusable function” or “convert this script to use hydra for configuration management” and Cursor performs complex refactoring that would be tedious manually.

Codebase Understanding: “Where is the data augmentation code?” or “How is early stopping implemented?” and Cursor searches your entire project, understanding code semantically rather than just keyword matching.

This deeper integration accelerates ML development beyond what’s possible with Jupyter’s cell-based AI assistance.

When to Use Each Tool

Rather than viewing these as competing tools, understand their complementary strengths.

Use Jupyter When:

• Initial data exploration: Understanding a new dataset, identifying patterns, checking data quality
• Prototyping and hypothesis testing: Quickly trying different approaches to see what works
• Visualization-heavy analysis: Creating plots and visual analyses that need immediate feedback
• Teaching and communication: Creating tutorials, documenting analyses, or presenting findings
• One-off analyses: Ad-hoc investigations that won’t be repeated or productionized
• Interactive debugging: Inspecting intermediate results step-by-step during development

Use Cursor When:

• Building production-ready models: Code that will eventually deploy to production
• Large-scale ML projects: Projects with multiple models, datasets, and team members
• Refactoring and optimization: Improving code structure and performance
• Testing and validation: Writing unit tests and integration tests for ML components
• Long-running training jobs: Training that needs to run reliably for hours or days
• CI/CD integration: ML pipelines that need to run automatically
• Team collaboration: Projects where multiple people work on shared codebase

The Optimal Workflow:

Many successful ML teams use both:

1. Exploration in Jupyter: Initial data analysis, trying different features, prototyping models
2. Refinement in Cursor: Taking successful experiments and converting to well-structured code
3. Development in Cursor: Building out training pipelines, evaluation frameworks, serving infrastructure
4. Occasional Jupyter use: Quick analyses of model behavior, one-off visualizations, debugging specific issues

This hybrid approach leverages each tool’s strengths without being constrained by their limitations.

Conclusion

The choice between Cursor and Jupyter for machine learning isn’t binary—these tools serve different but complementary roles in the ML development lifecycle. Jupyter excels at the exploratory, experimental phase where rapid iteration, immediate visual feedback, and flexible execution order accelerate discovery and learning. Its notebook format is unmatched for communicating findings, creating tutorials, and documenting the analytical journey from raw data to insights. However, as projects mature toward production, Jupyter’s informal structure becomes a liability—version control friction, collaboration challenges, and the gap between notebook code and production requirements create significant overhead.

Cursor, representing the modern AI-powered code editor approach, brings software engineering discipline to ML development. Its structured file organization, AI assistance that understands your entire codebase, and production-ready code patterns make it superior for building robust, maintainable ML systems that will deploy to real applications. The learning curve is steeper than Jupyter’s immediate accessibility, and the experimental feedback loop is longer, but the payoff in code quality, team collaboration, and production readiness is substantial. The most effective ML practitioners don’t choose between these tools—they use Jupyter for exploration and Cursor for implementation, combining exploratory power with engineering rigor to build ML systems that are both innovative and reliable.