Best Artificial Intelligence Software for Jupyter Notebook - Page 2
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Compare the Top Artificial Intelligence Software that integrates with Jupyter Notebook as of November 2025 - Page 2
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This a list of Artificial Intelligence software that integrates with Jupyter Notebook. Use the filters on the left to add additional filters for products that have integrations with Jupyter Notebook. View the products that work with Jupyter Notebook in the table below.
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runcell.dev
runcell.dev
Runcell is a Jupyter-native AI agent that understands your notebooks, writes code and executes cells so you can focus on insights, offering four AI-powered modes in one high-performance extension: Interactive Learning Mode provides an AI teacher that explains concepts with live code examples, step-by-step algorithm comparisons and real-time visual execution; Autonomous Agent Mode takes full control of your notebook to execute cells, automate complex workflows, reduce manual tasks and handle errors intelligently; Smart Edit Mode acts as a context-aware assistant, delivering intelligent code suggestions, automated optimizations and real-time syntax and logic improvements; and AI-Enhanced Jupyter lets you ask natural-language questions about your code, generate AI-powered solutions and receive smart recommendations for next steps, all seamlessly integrated into the familiar Jupyter interface.Starting Price: $20 per month -
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Google Cloud Datalab
Google
An easy-to-use interactive tool for data exploration, analysis, visualization, and machine learning. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks. Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly. -
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Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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JetBrains DataSpell
JetBrains
Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.Starting Price: $229 -
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Chalk
Chalk
Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.Starting Price: Free -
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Apolo
Apolo
Access readily available dedicated machines with pre-configured professional AI development tools, from dependable data centers at competitive prices. From HPC resources to an all-in-one AI platform with an integrated ML development toolkit, Apolo covers it all. Apolo can be deployed in a distributed architecture, as a dedicated enterprise cluster, or as a multi-tenant white-label solution to support dedicated instances or self-service cloud. Right out of the box, Apolo spins up a full-fledged AI-centric development environment with all the tools you need at your fingertips. Apolo manages and automates the infrastructure and processes for successful AI development at scale. Apolo's AI-centric services seamlessly stitch your on-prem and cloud resources, deploy pipelines, and integrate your open-source and commercial development tools. Apolo empowers enterprises with the tools and resources necessary to achieve breakthroughs in AI.Starting Price: $5.35 per hour -
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DagsHub
DagsHub
DagsHub is a collaborative platform designed for data scientists and machine learning engineers to manage and streamline their projects. It integrates code, data, experiments, and models into a unified environment, facilitating efficient project management and team collaboration. Key features include dataset management, experiment tracking, model registry, and data and model lineage, all accessible through a user-friendly interface. DagsHub supports seamless integration with popular MLOps tools, allowing users to leverage their existing workflows. By providing a centralized hub for all project components, DagsHub enhances transparency, reproducibility, and efficiency in machine learning development. DagsHub is a platform for AI and ML developers that lets you manage and collaborate on your data, models, and experiments, alongside your code. DagsHub was particularly designed for unstructured data for example text, images, audio, medical imaging, and binary files.Starting Price: $9 per month -
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AWS Marketplace
Amazon
AWS Marketplace is a curated digital catalog that enables customers to discover, purchase, deploy, and manage third-party software, data products, AI agents, and services directly within the AWS ecosystem. It provides access to thousands of listings across categories like security, machine learning, business applications, and DevOps tools. With flexible pricing models such as pay-as-you-go, annual subscriptions, and free trials, AWS Marketplace simplifies procurement and billing by integrating costs into a single AWS invoice. It also supports rapid deployment with pre-configured software that can be launched on AWS infrastructure. This streamlined approach allows businesses to accelerate innovation, reduce time-to-market, and maintain better control over software usage and costs. -
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NeevCloud
NeevCloud
NeevCloud delivers cutting-edge GPU cloud solutions powered by NVIDIA GPUs like the H200, H100, GB200 NVL72, and many more offering unmatched performance for AI, HPC, and data-intensive workloads. Scale dynamically with flexible pricing and energy-efficient GPUs that reduce costs while maximizing output. Ideal for AI model training, scientific research, media production, and real-time analytics, NeevCloud ensures seamless integration and global accessibility. Experience unparalleled speed, scalability, and sustainability with NeevCloud GPU cloud solutions.Starting Price: $1.69/GPU/hour -
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E2E Cloud
E2E Networks
E2E Cloud provides advanced cloud solutions tailored for AI and machine learning workloads. We offer access to cutting-edge NVIDIA GPUs, including H200, H100, A100, L40S, and L4, enabling businesses to efficiently run AI/ML applications. Our services encompass GPU-intensive cloud computing, AI/ML platforms like TIR built on Jupyter Notebook, Linux and Windows cloud solutions, storage cloud with automated backups, and cloud solutions with pre-installed frameworks. E2E Networks emphasizes a high-value, top-performance infrastructure, boasting a 90% cost reduction in monthly cloud bills for clients. Our multi-region cloud is designed for performance, reliability, resilience, and security, serving over 15,000 clients. Additional features include block storage, load balancers, object storage, one-click deployment, database-as-a-service, API & CLI access, and a content delivery network.Starting Price: $0.012 per hour -
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Azure Notebooks
Microsoft
Develop and run code from anywhere with Jupyter notebooks on Azure. Get started for free. Get a better experience with a free Azure Subscription. Perfect for data scientists, developers, students, or anyone. Develop and run code in your browser regardless of industry or skillset. Supporting more languages than any other platform including Python 2, Python 3, R, and F#. Created by Microsoft Azure: Always accessible, always available from any browser, anywhere in the world. -
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Kaggle
Kaggle
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 19,000 public datasets and 200,000 public notebooks to conquer any analysis in no time. -
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Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
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Fosfor Decision Cloud
Fosfor
Everything you need to make better business decisions. The Fosfor Decision Cloud unifies the modern data ecosystem to deliver the long-sought promise of AI: enhanced business outcomes. The Fosfor Decision Cloud unifies the components of your data stack into a modern decision stack, built to amplify business outcomes. Fosfor works seamlessly with its partners to create the modern decision stack, which delivers unprecedented value from your data investments. -
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Zepl
Zepl
Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github. -
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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Amazon SageMaker Studio
Amazon
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. -
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Amazon SageMaker Studio Lab
Amazon
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately. -
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EdgeCortix
EdgeCortix
Breaking the limits in AI processors and edge AI inference acceleration. Where AI inference acceleration needs it all, more TOPS, lower latency, better area and power efficiency, and scalability, EdgeCortix AI processor cores make it happen. General-purpose processing cores, CPUs, and GPUs, provide developers with flexibility for most applications. However, these general-purpose cores don’t match up well with workloads found in deep neural networks. EdgeCortix began with a mission in mind: redefining edge AI processing from the ground up. With EdgeCortix technology including a full-stack AI inference software development environment, run-time reconfigurable edge AI inference IP, and edge AI chips for boards and systems, designers can deploy near-cloud-level AI performance at the edge. Think about what that can do for these and other applications. Finding threats, raising situational awareness, and making vehicles smarter. -
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Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
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3LC
3LC
Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility. -
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MinusX
MinusX
A Chrome extension that operates your analytics apps for you. MinusX is the fastest way to get insights from data. Interop with MinusX to modify or extend existing notebooks. Select an area and ask questions, or ask for modifications. MinusX works in your existing analytics tools like Jupyter Notebooks, Metabase, Tableau, etc. You can use minusx to create analyses and share results with your team, instantly. We have nuanced privacy controls on MinusX. Any data you share, will be used to train better, more accurate models). We never share your data with third parties. MinusX seamlessly integrates with existing tools. This means that you never have to get out of your workflow to answer questions. Since actions are first-class entities, MinusX can choose the right action for the right context. Currently, we support Claude Sonnet 3.5, GPT-4o and GPT-4o mini. We are also working on a way to let you bring your own models. -
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ModelOp
ModelOp
ModelOp is the leading AI governance software that helps enterprises safeguard all AI initiatives, including generative AI, Large Language Models (LLMs), in-house, third-party vendors, embedded systems, etc., without stifling innovation. Corporate boards and C‑suites are demanding the rapid adoption of generative AI but face financial, regulatory, security, privacy, ethical, and brand risks. Global, federal, state, and local-level governments are moving quickly to implement AI regulations and oversight, forcing enterprises to urgently prepare for and comply with rules designed to prevent AI from going wrong. Connect with AI Governance experts to stay informed about market trends, regulations, news, research, opinions, and insights to help you balance the risks and rewards of enterprise AI. ModelOp Center keeps organizations safe and gives peace of mind to all stakeholders. Streamline reporting, monitoring, and compliance adherence across the enterprise. -
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NVIDIA Morpheus
NVIDIA
NVIDIA Morpheus is a GPU-accelerated, end-to-end AI framework that enables developers to create optimized applications for filtering, processing, and classifying large volumes of streaming cybersecurity data. Morpheus incorporates AI to reduce the time and cost associated with identifying, capturing, and acting on threats, bringing a new level of security to the data center, cloud, and edge. Morpheus also extends human analysts’ capabilities with generative AI by automating real-time analysis and responses, producing synthetic data to train AI models that identify risks accurately and run what-if scenarios. Morpheus is available as open-source software on GitHub for developers interested in using the latest pre-release features and who want to build from source. Get unlimited usage on all clouds, access to NVIDIA AI experts, and long-term support for production deployments with a purchase of NVIDIA AI Enterprise. -
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AutoKeras
AutoKeras
An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras supports several tasks with an extremely simple interface. -
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Clore.ai
Clore.ai
Clore.ai is a decentralized platform that revolutionizes GPU leasing by connecting server owners with renters through a peer-to-peer marketplace. It offers flexible, cost-effective access to high-performance GPUs for tasks such as AI development, scientific research, and cryptocurrency mining. Users can choose between on-demand leasing, which ensures uninterrupted computing power, and spot leasing, which allows for potential interruptions at a lower cost. It utilizes Clore Coin (CLORE), an L1 Proof of Work cryptocurrency, to facilitate transactions and reward participants, with 40% of block rewards directed to GPU hosts. This structure enables hosts to earn additional income beyond rental fees, enhancing the platform's appeal. Clore.ai's Proof of Holding (PoH) system incentivizes users to hold CLORE coins, offering benefits like reduced fees and increased earnings. It supports a wide range of applications, including AI model training, scientific simulations, etc. -
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Beam Cloud
Beam Cloud
Beam is a serverless GPU platform designed for developers to deploy AI workloads with minimal configuration and rapid iteration. It enables running custom models with sub-second container starts and zero idle GPU costs, allowing users to bring their code while Beam manages the infrastructure. It supports launching containers in 200ms using a custom runc runtime, facilitating parallelization and concurrency by fanning out workloads to hundreds of containers. Beam offers a first-class developer experience with features like hot-reloading, webhooks, and scheduled jobs, and supports scale-to-zero workloads by default. It provides volume storage options, GPU support, including running on Beam's cloud with GPUs like 4090s and H100s or bringing your own, and Python-native deployment without the need for YAML or config files. -
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NEO
NEO
NEO is an autonomous machine learning engineer: a multi-agent system that automates the entire ML workflow so that teams can delegate data engineering, model development, evaluation, deployment, and monitoring to an intelligent pipeline without losing visibility or control. It layers advanced multi-step reasoning, memory orchestration, and adaptive inference to tackle complex problems end-to-end, validating and cleaning data, selecting and training models, handling edge-case failures, comparing candidate behaviors, and managing deployments, with human-in-the-loop breakpoints and configurable enablement controls. NEO continuously learns from outcomes, maintains context across experiments, and provides real-time status on readiness, performance, and issues, effectively creating a self-driving ML engineering stack that surfaces insights, resolves standard settlement-style friction (e.g., conflicting configurations or stale artifacts), and frees engineers from repetitive grunt work. -
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Betteromics
Betteromics
Betteromics is deployed as a Private SaaS in your VPC so you can draw connections on all your data. Reproducibly validate your structured and unstructured data using configurable rules. Trace and audit your data from input to analysis with complete data provenance. Use natural language processing and large language models to abstract data elements from clinical records for QC, labeling, and analysis. Quickly develop and tune models specific to your task/data: detect anomalies, make predictions, understand your data, and optimize your processes. Enhance and complement your analysis and machine learning with integration-ready public datasets. Clinical-grade security including full encryption, data traceability, and role-based access controls. -
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CodeSquire
CodeSquire
Quickly write code by translating your comments into code, like in this example where we quickly create a Plotly bar chart. Create entire functions with ease, without searching for library methods and parameters. In this example, we created a function that loads df to AWS bucket in parquet format. Write SQL queries by providing CodeSquire with simple instructions on what you want to pull, join, and group by, like in the following example where we are trying to determine the top 10 most common names. CodeSquire can even help you understand someone else’s code, just ask to explain the function above, and get your explanation in plain text. CodeSquire can help you create complex functions that involve several logic steps. Brainstorm with it by starting simple and adding more complex features as you go.
