What is Jupyter NotebookWhat is Jupyter Notebook

At its core, Jupyter Notebook is a web interface for interactive programming and data analysis. Cells can run code written in Python, R, Julia, Scala or virtually any other language supported by Jupyter kernels while also holding visual outputs, mathematical formulas and narrative text. This flexibility makes notebooks equally useful for testing algorithms, analyzing results and documenting the reasoning behind them.

Technically, a Jupyter Notebook is a JSON file with the .ipynb extension. It stores three types of cells — code, markdown and raw text — along with their contents, outputs and metadata. Behind the scenes, a notebook connects to a kernel that manages the runtime session. Any variables or objects defined in one cell persist across others until the kernel is restarted.

For machine learning workflows, this turns a notebook into a live experiment record. Data preparation, feature engineering, training runs, metric tracking and result visualization all happen in the same space. Each step is preserved alongside its outputs, which means rerunning the notebook can fully reproduce the experiment from beginning to end.

Jupyter Notebook is just one of many tools in this category. Alternatives like Deepnote, as well as enterprise-grade solutions, provide similar notebook-style environments while adding features such as integration with clusters, version control systems and CI/CD pipelines. Together, they form a class of interactive notebooks designed to let users execute code step by step, capture results and supplement them with explanations.

Key features of Jupyter NotebookKey features of Jupyter Notebook

When applied to machine learning and especially LLM development, Jupyter’s usefulness stems from four core features: interactive code execution, immediate feedback, markdown-based documentation and native visualization integration.

In-line code executionIn-line code execution

Code in Jupyter runs cell by cell. You can modify snippets, execute them in any order and immediately see results. Because the kernel maintains session state, variables and objects persist across cells, it enables fast iteration without restarting pipelines. This workflow makes debugging and hypothesis testing more efficient — for example, when experimenting with image augmentation strategies, you can repeatedly adjust transformations and observe their effect without rewriting the rest of the code.

Real-time output and visualsReal-time output and visuals

Execution results are rendered inline as text, tables, static plots or interactive widgets. This feedback loop is particularly valuable in deep learning, where catching data or training issues early can prevent costly errors before large-scale runs. Analysts and engineers alike can visualize distributions, training curves or model predictions on the fly, turning abstract metrics into intuitive insights.

Markdown and documentation supportMarkdown and documentation support

Jupyter goes beyond code: it includes markdown cells for text, math formulas and rich formatting. This allows you to annotate experiments, add commentary to results and structure workflows as both runnable code and human-readable documentation. For research groups such as in-house AI R&D team at Nebius, this is vital — every step, observation and decision remains in a single, shareable document.

Integration with visualization librariesIntegration with visualization libraries

The environment works seamlessly with visualization packages such as matplotlib, seaborn and plotly. Their outputs appear directly within the notebook, whether as static charts or interactive visualizations. This allows ML engineers to quickly analyze feature distributions, track model performance or project embeddings — without leaving the notebook or switching tools.

What Jupyter Notebook is used forWhat Jupyter Notebook is used for

Jupyter supports a broad spectrum of machine learning tasks, from exploratory analysis to building production-ready models. Its strength lies in flexibility and interactivity, making it the go-to tool for scenarios where iteration and rapid feedback matter most.

Data analysis and visualizationData analysis and visualization

Notebooks are frequently used for exploratory data analysis. Step by step, you can load datasets, clean and transform them and immediately inspect distributions or correlations. This workflow accelerates discovery of patterns and anomalies before model training even begins.

With libraries like SciPy or statsmodels, statistical testing and regression analysis are also straightforward, producing clean outputs directly in the notebook. For engineers, this speeds up validation of dataset integrity; for analysts, it provides a transparent, visual record of insights.

Machine learning developmentMachine learning development

Jupyter is the preferred environment for prototyping ML models and debugging pipelines. Within a single notebook, you can handle dataset loading, construct a baseline model, experiment with hyperparameters and track validation metrics. Since cells run independently, parts of the pipeline can be modified and tested without re-running everything. This shortens iteration cycles, enabling faster exploration of architectures and parameter settings. Once a strong baseline is found, the logic can be refactored into modular code for production.

Educational and research useEducational and research use

In academic and research contexts, Jupyter merges code and explanations into one reproducible document. Teachers use it to demonstrate algorithms with narrative commentary and live examples. Researchers use it to publish experiments that include both methods and outputs — like the LIGO/Virgo collaboration, which shared full workflows for gravitational wave data analysis. This ensures reproducibility: anyone opening the notebook can rerun the same steps and verify findings independently.

Why AI researchers use Jupyter NotebooksWhy AI researchers use Jupyter Notebooks

In AI research, Jupyter has become the default environment for prototyping and experiment logging. Its architecture makes it easy to run code incrementally, record results and commentary inline and preserve entire workflows in a single file. The result is an ecosystem where experiments are more reproducible, transparent and collaborative.

Hugging Face, for instance, provides a library of official notebooks covering transformers, datasets and tokenizers, with detailed walk-throughs for fine-tuning language models. Mozilla Common Voice uses Jupyter to build and refine pipelines for audio data cleaning and model optimization.

Flexibility for experimentationFlexibility for experimentation

Cell-based execution lets researchers tweak parameters, rerun targeted sections and test ideas without restarting full pipelines. This is particularly powerful for hyperparameter tuning or architectural exploration, where dozens of variations can be preserved within a single notebook. Within one session, it’s possible to compare a classical ML model to a neural network or benchmark via multiple regularization strategies side by side.

Integration with ML librariesIntegration with ML libraries

Jupyter is tightly integrated with modern ML frameworks like TensorFlow, PyTorch, scikit-learn, Hugging Face and RAPIDS. This makes it possible to manage datasets, train models and build visualizations in one environment. Built-in GPU support further streamlines deep learning workflows: engineers can run accelerated training jobs and instantly review metrics.

Reproducibility and collaborationReproducibility and collaboration

Each notebook stores not just the code but also the order of execution and the outputs. This ensures reproducibility — colleagues can open the notebook, rerun cells and obtain comparable results. For team collaboration, notebooks can be versioned in Git, exported to formats like HTML or PDF or shared via GitHub and cloud services. They also integrate into CI/CD systems: automated execution of notebooks can validate code correctness and outputs alongside other components of the ML pipeline, turning notebooks into reliable checkpoints in larger workflows.

How to use Jupyter Notebook for AI workflowsHow to use Jupyter Notebook for AI workflows

Although Jupyter began as a lightweight tool for idea testing, it is now firmly embedded in modern ML development. To use it effectively, it’s essential to understand both its strengths and how it integrates into larger infrastructure.

Running a Notebook locally or in the cloudRunning a Notebook locally or in the cloud

Locally, Jupyter can be launched through environments managed by venv, Conda or Docker, with interfaces like JupyterLab, the classic Notebook or the VS Code Jupyter extension. Users install their own libraries, CUDA runtime and drivers. The main limitation of this setup is scalability: computation is restricted to the resources of a single machine and GPU clustering requires external integration.

In cloud environments such as Nebius AI Cloud, the notebook kernel runs on remote servers with direct access to GPU resources. These setups typically come with preconfigured environments that include CUDA, drivers and ML libraries, minimizing manual setup. Data resides in connected storage and multiple users can collaborate on the same project simultaneously. This model eliminates environment management overhead and provides access to far greater compute resources than most local machines.

Basic workflow stepsBasic workflow steps

In practice, a notebook usually serves as the backbone of an AI workflow. Things often start with bringing in the dataset — whether it’s stored in an object store, a database or a remote repository. From there, the data goes through preprocessing: cleaning, filtering, normalizing features and sometimes generating new ones. For this step, pandas works well with smaller datasets, while tools like Dask or PySpark help when the data volume grows.

With the data prepared, the next step is setting up the model. Classical machine learning tasks are often handled with scikit-learn or XGBoost while deep learning projects rely on frameworks like PyTorch or TensorFlow — both of which can scale across multiple GPUs when needed. Jupyter makes it straightforward to experiment with model training: you can tweak batch sizes, adjust learning rate schedules or change the number of epochs and immediately see the effect on your metrics.

Evaluation comes next. Depending on the task, this might mean standard metrics like accuracy, recall or F1; aggregate ones such as AUC or mAP; or specialized metrics like perplexity for language models or WER for speech recognition.

The last piece is documentation. A solid workflow keeps track of hyperparameters, library versions and model checkpoints while saving the notebook to Git and exporting reports in formats like HTML or PDF. That way, you can always revisit specific steps without rerunning the entire pipeline — and your work remains reproducible, even if the data or algorithms change down the line.

Example use cases in AIExample use cases in AI

Jupyter is particularly valuable for prototyping applied AI solutions. Within a single notebook, you can design a recommendation system, perform image classification or fine-tune a language model on domain-specific data. The resulting document remains fully executable, making it simple to share progress with colleagues, stakeholders or customers. In research, notebooks often serve as structured experiment logs. They allow teams to compare multiple architectures, benchmark hyperparameter configurations or document ablation studies step by step. This structured format makes it easier to move successful ideas into production pipelines while also preserving institutional knowledge for the future.

Limitations of Jupyter NotebooksLimitations of Jupyter Notebooks

Despite their strengths, Jupyter Notebooks are not designed for production deployment. They lack support for streaming workloads, automated deployment and service-level fault tolerance. Code inside notebooks is often fragmented, not covered by tests and rarely tied into CI/CD pipelines.

Not ideal for productionNot ideal for production

Notebook code tends to be ad hoc, loosely structured and split across cells rather than organized into reusable modules. Moving a model into production usually requires rewriting the notebook into packages or services, adding proper testing and integrating with CI/CD systems. Production environments also rely on strict dependency management and monitoring — capabilities notebooks do not natively provide.

Versioning and code quality challengesVersioning and code quality challenges

Version control is another pain point. Because notebook execution depends on cell order, the same file can produce different results for different users. Git tracks notebooks as JSON, which makes diffs hard to interpret and collaboration harder to manage. Over time, notebooks often accumulate poor coding practices: redefined variables, duplicated functions and unrelated experiments mixed together. This lowers overall code quality and complicates long-term maintenance.

Jupyter Notebook by default runs within a single kernel and does not include built-in tools for distributed computing. However, it can be extended to work with external systems such as Slurm, Kubernetes, PyTorch DDP or Horovod. When training on dozens of GPUs, orchestrating synchronized runs or ensuring integration with cluster schedulers, Jupyter alone quickly reaches its limits. In practice, notebooks are often connected to Kubernetes, Slurm or cloud services, but this requires additional setup and integration. Without it, Jupyter becomes a bottleneck: convenient for testing ideas but lacking the scalability required for production-grade AI workflows.