Jupyter Notebook Alternatives

At Posit, our goal is to make data science more open, intuitive, accessible, and collaborative. We provide tools that make it easy for individuals, teams, and enterprises to leverage powerful analytics and gain the insights they need to make a lasting impact. From the beginning, we’ve invested in open-source software like the RStudio IDE, Shiny, and tidyverse. Because we believe in putting the power of data science tools in the hands of everyone. We develop R and Python-based tools to help you produce higher-quality analysis faster. Securely share data-science applications across your team and the enterprise. Our code is your code. Build on it. Share it. Improve people’s lives with it. Take the time and effort out of uploading, storing, accessing, and sharing your work. We love hearing about the amazing work being done with our tools around the world. And we really love sharing those stories.

Composable is an enterprise-grade DataOps platform built for business users that want to architect data intelligence solutions and deliver operational data-driven products leveraging disparate data sources, live feeds, and event data regardless of the format or structure of the data. With a modern, intuitive dataflow visual designer, built-in services to facilitate data engineering, and a composable architecture that enables abstraction and integration of any software or analytical approach, Composable is the leading integrated development environment to discover, manage, transform and analyze enterprise data.

Streamlit. The fastest way to build and share data apps. Turn data scripts into sharable web apps in minutes. All in Python. All for free. No front-end experience required. Streamlit combines three simple ideas. Embrace Python scripting. Build an app in a few lines of code with our magically simple API. Then see it automatically update as you save the source file. Weave in interaction. Adding a widget is the same as declaring a variable. No need to write a backend, define routes, handle HTTP requests, etc. Deploy instantly. Use Streamlit’s sharing platform to effortlessly share, manage, and collaborate on your apps. A minimal framework for powerful apps. Face-GAN explorer. App that uses Shaobo Guan’s TL-GAN project from Insight Data Science, TensorFlow, and NVIDIA's PG-GAN to generate faces that match selected attributes. Real time object detection. An image browser for the Udacity self-driving-car dataset with real-time object detection.

VSCode: Code editing. Redefined. Free. Built on open source. Runs everywhere. Go beyond syntax highlighting and autocomplete with IntelliSense, which provides smart completions based on variable types, function definitions, and imported modules. Debug code right from the editor. Launch or attach to your running apps and debug with break points, call stacks, and an interactive console. Working with Git and other SCM providers has never been easier. Review diffs, stage files, and make commits right from the editor. Push and pull from any hosted SCM service. Want even more features? Install extensions to add new languages, themes, debuggers, and to connect to additional services. Extensions run in separate processes, ensuring they won't slow down your editor. Learn more about extensions. With Microsoft Azure you can deploy and host your React, Angular, Vue, Node, Python (and more!) sites, store and query relational and document based data, and scale with serverless computing.

R Markdown documents are fully reproducible. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL. R Markdown supports dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more. R Markdown provides an authoring framework for data science. You can use a single R Markdown file to both. When you open the file in the RStudio IDE, it becomes a notebook interface for R. You can run each code chunk by clicking the icon. RStudio executes the code and display the results inline with your file.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

The world's definitive system for modern technical computing. For three decades, Mathematica has defined the state of the art in technical computing—and provided the principal computation environment for millions of innovators, educators, students, and others around the world. Widely admired for both its technical prowess and elegant ease of use, Mathematica provides a single integrated, continually expanding system that covers the breadth and depth of technical computing—and is seamlessly available in the cloud through any web browser, as well as natively on all modern desktop systems. With energetic development and consistent vision for three decades, Mathematica stands alone in a huge range of dimensions, unique in its support for today's technical computing environments and workflows.

Made by industry leaders. Battle tested at the world's largest tech companies. Connect your people and your data. Put data in the hands of every employee. Save by retiring on-prem infrastructure. Multiply your data team's productivity. We have a long history of supporting technical communities and open source initiatives. We value the energy, open standards, and exchange of ideas that emerge when passionate professionals come together for a common cause. That’s why Noteable’s committed to supporting technical communities and contributing back to open source wherever possible. Noteable is your data platform that transform how data teams work by delivering modern collaboration securely and cohesively across all your users. Multi-tenant cloud or deploy to a single-tenant virtual private cluster. You’re in complete control of geography, networking setup, and more. You define all the rules of your cloud.

Teaching scientific software online. CoCalc is a virtual online computer lab: it takes away the pain of teaching scientific software. Every student works 100% online – inside their own, isolated workspace. Follow the progress of each student in real-time. At any time you can jump into a file of a student, right where they are working. Use TimeTravel to see each step a student took to get to the solution. Integrated chat rooms allows you to guide students directly where they work or discuss collected files with your teaching assistants. The project's Activity Log records exactly when and by whom a file was accessed. Forget any complicated software setup – everyone is able to start working in seconds! Since everyone works with exactly the same software stack, any inconsistencies between your and your students' environments are eliminated.

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.

Colaboratory, or "Colab" for short, allows you to write and execute Python in your browser, with - Zero configuration required - Free access to GPUs - Easy sharing Whether you're a student, a data scientist or an AI researcher, Colab can make your work easier. Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX, and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them.

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.

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.

Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more. IPython interpreter provides comparable user experience like Jupyter Notebook. This release includes Note level dynamic form, note revision comparator and ability to run paragraph sequentially, instead of simultaneous paragraph execution in previous releases. Interpreter lifecycle manager automatically terminate interpreter process on idle timeout. So resources are released when they're not in use.

IDLE is Python’s Integrated Development and Learning Environment. IDLE has the following features: - coded in 100% pure Python, using the tkinter GUI toolkit - cross-platform: works mostly the same on Windows, Unix, and macOS - Python shell window (interactive interpreter) with colorizing of code input, output, and error messages - multi-window text editor with multiple undo, Python colorizing, smart indent, call tips, auto completion, and other features - search within any window, replace within editor windows, and search through multiple files (grep) - debugger with persistent breakpoints, stepping, and viewing of global and local namespaces - configuration, browsers, and other dialogs

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.

Understand how users are interacting with your product and identify opportunity areas to inform your product decisions. Mode empowers one Stitch analyst to do the work of a full data team through speed, flexibility, and collaboration. Build dashboards for annual revenue, then use chart visualizations to identify anomalies quickly. Create polished, investor-ready reports or share analysis with teams for collaboration. Connect your entire tech stack to Mode and identify upstream issues to improve performance. Speed up workflows across teams with APIs and webhooks. Understand how users are interacting with your product and identify opportunity areas to inform your product decisions. Leverage marketing and product data to fix weak spots in your funnel, improve landing-page performance, and understand churn before it happens.

Org is implemented on top of Outline mode, which makes it possible to keep the content of large files well structured. Visibility cycling and structure editing help to work with the tree. Tables are easily created with a built-in table editor. Plain text URL-like links connect to websites, emails, Usenet messages, BBDB entries, and any files related to the projects. Org develops organizational tasks around notes files that contain lists or information about projects as plain text. Project planning and task management make use of metadata which is part of an outline node. Based on this data, specific entries can be extracted in queries and create dynamic agenda views that also integrate the Emacs calendar and diary. Org can be used to implement many different project planning schemes, such as David Allen’s GTD system. Org files can serve as a single source authoring system with export to many different formats such as HTML, LaTeX, Open Document, and Markdown.

All the Python tools in one place. Save time while PyCharm takes care of the routine. Focus on the bigger things and embrace the keyboard-centric approach to get the most of PyCharm's many productivity features. PyCharm knows everything about your code. Rely on it for intelligent code completion, on-the-fly error checking and quick-fixes, easy project navigation, and much more. Write neat and maintainable code while the IDE helps you keep control of the quality with PEP8 checks, testing assistance, smart refactorings, and a host of inspections. PyCharm is designed by programmers, for programmers, to provide all the tools you need for productive Python development. PyCharm provides smart code completion, code inspections, on-the-fly error highlighting and quick-fixes, along with automated code refactorings and rich navigation capabilities.

The entire process of building machine learning algorithms and predicting outcomes, packed in one single click. Not all data is built to be ready for ML, use the Data Dialog to seamlessly shape your dataset without wrangling your files. Share your prediction reports with your team or make them public. Allow anyone to start making predictions on your model. Bring dynamic ML predictions into your own app using our low-code API. Predict willingness to pay, score leads and much more in real-time. Obviously AI puts the world’s most cutting-edge algorithms in your hands, without compromising on performance. Forecast revenue, optimize supply chain, personalize marketing. You can now know what happens next. Add a CSV file OR integrate with your favorite data sources in minutes. Pick your prediction column from a dropdown, we'll auto build the AI. Beautifully visualize predicted results, top drivers and simulate "what-if" scenarios.

IBM Analytics for Apache Spark is a flexible and integrated Spark service that empowers data science professionals to ask bigger, tougher questions, and deliver business value faster. It’s an easy-to-use, always-on managed service with no long-term commitment or risk, so you can begin exploring right away. Access the power of Apache Spark with no lock-in, backed by IBM’s open-source commitment and decades of enterprise experience. A managed Spark service with Notebooks as a connector means coding and analytics are easier and faster, so you can spend more of your time on delivery and innovation. A managed Apache Spark services gives you easy access to the power of built-in machine learning libraries without the headaches, time and risk associated with managing a Sparkcluster independently.

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.

Access, explore and prepare data while discovering new trends and patterns. SAS Visual Data Science helps you create and share smart visualizations and interactive reports through a single, self-service interface. It uses machine learning, text analytics and econometrics capabilities for better forecasting and optimization, plus it manages and registers SAS and open-source models within projects or as standalone models. Visualize and discover relevant relationships in your data. Create and share interactive reports and dashboards, and use self-service analytics to quickly assess probable outcomes for smarter, more data-driven decisions. Explore data and build or adjust predictive analytical models with this solution running in SAS® Viya®. Data scientists, statisticians, and analysts can collaborate and iteratively refine models for each segment or group to make decisions based on accurate insights.

MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Automatically convert MATLAB algorithms to C/C++, HDL, and CUDA code to run on your embedded processor or FPGA/ASIC. MATLAB works with Simulink to support Model-Based Design.

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.

Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface.

Empowering the enterprise to do real data science at speed and scale with a full-featured machine learning platform. Spend less time managing tools and infrastructure, so you can focus on building machine learning applications that move your business forward. Anaconda Enterprise takes the headache out of ML operations, puts open-source innovation at your fingertips, and provides the foundation for serious data science and machine learning production without locking you into specific models, templates, or workflows. Software developers and data scientists can work together with AE to build, test, debug, and deploy models using their preferred languages and tools. AE provides access to both notebooks and IDEs so developers and data scientists can work together more efficiently. They can also choose from example projects and preconfigured projects. AE projects are automatically containerized so they can be moved between environments with ease.

Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs.

Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.

Machine Learning and Predictive Analytics Platform. Augment data-driven decision making and accelerate business with advanced artificial intelligence in a pre-built machine learning and big data analytics platform. OpenText Magellan uses AI technologies to provide predictive analytics in easy to consume and flexible data visualizations that maximize the value of business intelligence. Artificial intelligence software eliminates the need for manual big data processing by presenting valuable business insights in a way that is accessible and related to the most critical objectives of the organization. By augmenting business processes through a curated mix of capabilities, including predictive modeling, data discovery tools, data mining techniques, IoT data analytics and more, organizations can use their data to improve decision making based on real business intelligence and analytics.

SAS® Viya® data science offerings provide a comprehensive, scalable analytics environment that's quick and easy to deploy, enabling you to meet diverse business needs. Automatically generated insights enable you to identify the most common variables across all models, the most important variables selected across models and assessment results for all models. Natural language generation capabilities are used to create project summaries written in plain language, enabling you to easily interpret reports. Analytics team members can add project notes to the insights report to facilitate communication and collaboration among team members. SAS lets you embed open source code within an analysis and call open source algorithms seamlessly within its environment. This facilitates collaboration across your organization because users can program in their language of choice. You can also take advantage of SAS Deep Learning with Python (DLPy), our open-source package on GitHub.

Run, manage, control and secure the apps, data and IT that run your business, from edge to cloud. HPE Ezmeral advances digital transformation initiatives by shifting time and resources from IT operations to innovations. Modernize your apps. Simplify your Ops. And harness data to go from insights to impact. Accelerate time-to-value by deploying Kubernetes at scale with integrated persistent data storage for app modernization on bare metal or VMs, in your data center, on any cloud or at the edge. Harness data and get insights faster by operationalizing the end-to-end process to build data pipelines. Bring DevOps agility to the machine learning lifecycle, and deliver a unified data fabric. Boost efficiency and agility in IT Ops with automation and advanced artificial intelligence. And provide security and control to eliminate risk and reduce costs. HPE Ezmeral Container Platform provides an enterprise-grade platform to deploy Kubernetes at scale for a wide range of use cases.

Datalore is a collaborative data science and analytics platform aimed at boosting the whole analytics workflow and making work with data enjoyable for both data scientists and data savvy business teams across the enterprise. Keeping a major focus on data teams workflow, Datalore offers technical-savvy business users the ability to work together with data teams, using no-code or low-code together with the power of Jupyter notebooks. Datalore enables analytical self-service for business users, enabling them to work with data using SQL and no-code cells, build reports and deep dive into data. It offloads the core data team with simple tasks. Datalore enables analysts and data scientists to share results with ML Engineers. You can run your code on powerful CPUs or GPUs and collaborate with your colleagues in real-time.

With SAS Visual Statistics, multiple users can explore data, then interactively create and refine predictive models. Your data scientists and statisticians can act on observations at a granular level using the most appropriate analytical modeling techniques. The result? You'll unearth insights at unprecedented speeds, and find new ways to grow revenue. Easily build and refine models to target specific groups or segments, and run numerous scenarios simultaneously. You can ask more what-if questions to get better results. And put results into action with an automatically generated score code. Empower multiple users to interact with data visually – to add or change variables, remove outliers, etc. Instantly see how changes affect your model's predictive power, and make refinements quickly. Data science teams have the ultimate flexibility of working in their language of choice, so they can use their skills to the fullest. SAS Visual Statistics unites all analytical assets.

Integrate analytics into real-time ​interactions and event-based capabilities​. SAS Visual Data Science Decisioning features robust data management, visualization, advanced analytics and model management. It supports decisions by creating, embedding and governing analytically driven decision flows at scale in real-time or batch. It also deploys analytics and decisions in the stream to help you discover insights. Solve complex analytical problems with a comprehensive visual interface that handles all tasks in the analytics life cycle. SAS Visual Data Mining and Machine Learning, which runs in SAS® Viya®, combines data wrangling, exploration, feature engineering, and modern statistical, data mining, and machine learning techniques in a single, scalable in-memory processing environment. Access data files, libraries and existing programs, or write new ones, with this developmental web application accessible through your browser.

Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. You can make copies of all the tutorials in your current directory using the metaflow command line interface.

Scale your machine learning development from research to production with an end-to-end solution that gives your data science team all the tools they need in one place. As the leading data science platform for MLOps and model management, cnvrg.io is a pioneer in building cutting-edge machine learning development solutions so you can build high-impact machine learning models in half the time. Bridge science and engineering teams in a clear and collaborative machine learning management environment. Communicate and reproduce results with interactive workspaces, dashboards, dataset organization, experiment tracking and visualization, a model repository and more. Focus less on technical complexity and more on building high impact ML models. Cnvrg.io container-based infrastructure helps simplify engineering heavy tasks like tracking, monitoring, configuration, compute resource management, serving infrastructure, feature extraction, and model deployment.

IBM SPSS Modeler is a leading visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations worldwide use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets. IBM SPSS Modeler automatically transforms data into the best format for the most accurate predictive modeling. It now only takes a few clicks for you to analyze data, identify fixes, screen out fields and derive new attributes. Leverage IBM SPSS Modeler’s powerful graphics engine to bring your insights to life. The smart chart recommender finds the perfect chart for your data from among dozens of options, so you can share your insights quickly and easily using compelling visualizations.

Metacoder makes processing data faster and easier. Metacoder gives analysts needed flexibility and tools to facilitate data analysis. Data preparation steps such as cleaning are managed reducing the manual inspection time required before you are up and running. Compared to alternatives, is in good company. Metacoder beats similar companies on price and our management is proactively developing based on our customers' valuable feedback. Metacoder is used primarily to assist predictive analytics professionals in their job. We offer interfaces for database integrations, data cleaning, preprocessing, modeling, and display/interpretation of results. We help organizations distribute their work transparently by enabling model sharing, and we make management of the machine learning pipeline easy to make tweaks. Soon we will be including code free solutions for image, audio, video, and biomedical data.

Enterprise-grade architecture and cloud data platform powered by Snowflake® to securely store and leverage your data in the cloud. Built-in ETL and drag-and-drop visual workflow designer to connect, clean & transform your data from 250+ data sources. Use the latest in Search and AI-driven technology to generate insights and actionable analytics from your data in seconds. Leverage our AI/ML environments to build, tune and deploy your models for predictive analytics and forecasting. Leverage our built-in AI/ML environments to take your data to the next level. Create, train, tune and deploy your AI models for predictive analysis and forecasting, using the PurpleCube Data Science module. Build BI visualizations with PurpleCube Analytics, search through your data using natural language, and leverage AI-driven insights and smart suggestions that deliver answers to questions you didn’t think to ask.

Predictive Modeling with Machine Learning and Explainable AI. FICO® Analytics Workbench™ is an integrated suite of state-of-the-art analytic authoring tools that empowers companies to improve business decisions across the customer lifecycle. With it, data scientists can build superior decisioning capabilities using a wide range of predictive data modeling tools and algorithms, including the latest machine learning (ML) and explainable artificial intelligence (xAI) approaches. We enhance the best of open source data science and machine learning with innovative intellectual property from FICO to deliver world-class analytic capabilities to discover, combine, and operationalize predictive signals in data. Analytics Workbench is built on the leading FICO® Platform to allow new predictive models and strategies to be deployed into production with ease.

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

H2O.ai is the open source leader in AI and machine learning with a mission to democratize AI for everyone. Our industry-leading enterprise-ready platforms are used by hundreds of thousands of data scientists in over 20,000 organizations globally. We empower every company to be an AI company in financial services, insurance, healthcare, telco, retail, pharmaceutical, and marketing and delivering real value and transforming businesses today.

Quadratic enables your team to work together on data analysis to deliver faster results. You already know how to use a spreadsheet, but you’ve never had this much power. Quadratic speaks Formulas and Python (SQL & JavaScript coming soon). Use the language you and your team already know. Single-line formulas are hard to read. In Quadratic you can expand your recipes to as many lines as you need. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. The last line of code is returned to the spreadsheet. Raw values, 1/2D arrays, and Pandas DataFrames are supported by default. Pull or fetch data from an external API, and it updates automatically in Quadratic's cells. Navigate with ease, zoom out for the big picture, and zoom in to focus on the details. Arrange and navigate your data how it makes sense in your head, not how a tool forces you to do it.

Whatever your business need, discover hidden insights quickly and easily using HyperCube, the platform designed for the way data scientists work. Put your business data to work. Unlock understanding, discover unrealized opportunities, generate predictions and avoid risks before they happen. HyperCube takes huge volumes of data and turns it into actionable insights. Whether a beginner in analytics or a machine learning expert, HyperCube is designed with you in mind. It is the Swiss Army knife of data science, combining proprietary and open source code to deliver a wide range of data analysis features straight out of the box or as business apps, customized just for you. We are constantly updating and perfecting our technology so we can deliver the most innovative, intuitive and adaptable results Choose from apps, data-as-a-services (DaaS) and vertical market solutions.

Easily extract, transform, and load (ETL) data for data science and analytics. Design code-free data flows into data lakes and data marts. Part of Oracle’s comprehensive portfolio of integration solutions. Intuitive user interface helps you configure integration parameters and automate data mapping between sources and targets. Use one of the out-of-the-box operators, such as a join, aggregate, or expression to shape your data. Maintain your processes centrally and use parameters to override specific configuration values at runtime. Users can interactively prepare their data and view transformation results to validate their processes. Boost productivity and fine-tune data flows on the fly, without waiting for an execution to complete. Avoid broken integration flows and reduce maintenance complexities when data schemas evolve.

A data science platform that improves productivity with unparalleled abilities. Build and evaluate higher-quality machine learning (ML) models. Increase business flexibility by putting enterprise-trusted data to work quickly and support data-driven business objectives with easier deployment of ML models. Using cloud-based platforms to discover new business insights. Building a machine learning model is an iterative process. In this ebook, we break down the process and describe how machine learning models are built. Explore notebooks and build or test machine learning algorithms. Try AutoML and see data science results. Build high-quality models faster and easier. Automated machine learning capabilities rapidly examine the data and recommend the optimal data features and best algorithms. Additionally, automated machine learning tunes the model and explains the model’s results.

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.

Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.

Saturn Cloud is an award-winning ML platform for any cloud with 100,000+ users, including NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. It is an all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Users can spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, build large language models, and more in a completely hosted environment. Data professionals can use your preferred languages, IDEs, and machine-learning libraries in Saturn Cloud. We offer full Git integration, shared custom images, and secure credential storage, making scaling and building your team in the cloud easy. We support the entire machine learning lifecycle from experimentation to production with features like jobs and deployments. These features and built-in tools are easily shareable within teams, so time is saved and work is reproducible.