Alternatives to Jupyter Notebook

Composable is an enterprise-grade DataOps platform designed for business users who want to build data-driven products and create data intelligence solutions. It can be used to design data-driven products that leverage disparate data sources, live streams, and event data, regardless of their format or structure. Composable offers a user-friendly, intuitive dataflow visual editor, built-in services that facilitate data engineering, as well as a composable architecture which allows abstraction and integration of any analytical or software approach. It is the best integrated development environment for discovering, managing, transforming, and analysing enterprise data.

One source of truth for R packages and Python packages RStudio is the preferred professional data science solution for every group. A Python and R integrated development environment with syntax-highlighting editor, console, and code execution. It also includes tools for workspace management, history, plotting, and plotting. You can publish and distribute data products throughout your organization. One-button deployment of Shiny applications and R Markdown reports, Jupyter Notebooks, etc. To increase reproducibility and reduce the time spent installing and troubleshooting R packages, you can control, organize, and manage your use of them. RStudio is committed to sustainable investment in open-source and free software for data science. RStudio has been certified as a B Corporation. This means that our open-source mission has been codified in our charter. Our professional software products are enterprise-ready and provide a modular platform that allows teams to adopt open-source data sciences at scale.

The definitive system for modern technical computing. Mathematica is the global standard for technical computing. It has been the main computing environment for millions of students, educators, and innovators around the globe for over three decades. Mathematica is widely admired for its technical prowess as well as its elegant ease-of-use. It seamlessly integrates all aspects of technical computing and is available in the cloud via any web browser as well as natively on any modern desktop system. Mathematica is a pioneer in technical computing support and workflows, thanks to its energetic development and consistent vision over three decades.

Code editing. Redefined Free. Open source. It runs everywhere. IntelliSense provides smart completions that go beyond syntax highlighting and autocomplete. It uses variable types, function definitions and imported modules to provide intelligent completions. You can debug code directly from the editor. You can attach or launch your apps, and debug with breakpoints, call stacks and an interactive console. It's never been easier to work with Git or other SCM providers. The editor allows you to review diffs and stage files, as well as make commits. Pull and push from any hosted SCM service. Want even more features? To add languages, themes, debuggers and connect to other services, install extensions. Extensions are separate processes that don't slow down your editor. Learn more about extensions. Microsoft Azure allows you to deploy and host your React (Angular), Vue, Node (and many more!) applications. Sites can store and query relational or document-based data and scale with serverless computing.

Made by industry experts. Tested at the largest tech companies in the world. Connect your people and connect your data. Every employee can access data. Reduce costs by retiring on-prem infrastructure. Multiply the productivity of your data team. We have a long history supporting open source projects and technical communities. We value the energy, open standards and exchange of ideas that result from passionate professionals coming together for a common cause. Noteable is committed to supporting technical communities, and contributing to open source whenever possible. Noteable is your data platform. It transforms the way data teams work by enabling modern collaboration securely and co-operatively among all your users. You can deploy to a multi-tenant cloud, or a single-tenant virtual-private cluster. You have complete control over the location, network setup, and other details. You set all rules for your cloud.

Web-based notebook that allows data-driven, interactive data analysis and collaborative documents with SQL and Scala. The IPython interpreter offers a similar user experience to Jupyter Notebook. This release features Note level dynamic form, note comparison comparator, and the ability to run paragraph sequentially instead of simultaneous execution in previous releases. Interpreter lifecycle manager automatically terminates interpreter process upon idle timeout. So resources are released when not in use.

Online teaching of scientific software. CoCalc is an online computer lab that eliminates the pain of teaching scientific programming. Each student works 100% online, in their own isolated workspace. You can track the progress of each student online in real-time. You can access a file from any student at any time, right from where they are working. TimeTravel allows you to see every step taken by a student to reach the solution. You can use integrated chat rooms to direct students to where they are working or to discuss the files you have collected with your teaching assistants. The Activity Log in the project records when and by whom files were accessed. No need to worry about complicated software configurations - everyone can start working in seconds! Everybody uses the exact same software stack so there are no inconsistencies between your environment and that of your students.

Jupyter notebooks for Azure allow you to develop and run code anywhere. Get started free. Azure Subscriptions are a great way to get a better user experience. This subscription is ideal for data scientists, students, and developers. No matter your industry or skill set, you can develop and run code from your browser. More languages supported than any other platform, including Python 2, Python 3 and R. Microsoft Azure: Always accessible and available from any browser anywhere in the world.

IDLE is Python’s Integrated Development and Learning Environment. IDLE features include: - 100% pure Python code using the tkinter GUI Toolkit - cross-platform : works largely the same on Windows Unix and macOS - Python shell (interactive interpreter), with colorizing code input, output and error messages - Multi-window text editing with multiple undos, Python colorizing and smart indent. - Search within any window, Replace within editor windows and search through multiple files using grep - debugger that allows you to step, view global and local namespaces, and set persistent breakpoints - Configuration, browsers and other dialogs

With a single keystroke, switch between editor and command modes. Use the arrow keys to navigate between cells. All the Jupyter shortcuts are available. Fully interactive outputs are available right under the cell. Editing code cells is easy with smart code completion, quick error checking and quick fixes, and easy navigation. You can connect to remote JupyterHub or JupyterLab servers from the IDE. Interactively run Python scripts and arbitrary expressions in a Python Console. You can see the outputs and the state variables in real time. Split Python scripts into code cells using the #%% separator, and run them individually in a Jupyter notebook. Interactive controls allow you to browse DataFrames or visualizations in real time. All popular Python scientific libraries, including Plotly and Altair, ipywidgets and others, are supported.

Learn how users interact with your product and identify opportunities to help you make product decisions. Mode allows one Stitch analyst to perform the work of a full-time data team with speed, flexibility, collaboration. Create dashboards for annual revenue and then use chart visualizations quickly to identify anomalies. Share analysis with teams to create polished reports that are investor-ready. Connect your entire tech stack with Mode to identify upstream issues and improve performance. With webhooks and APIs, you can speed up team workflows. Learn how users interact with your product and identify areas for improvement. Use marketing and product data to identify weak points in your funnel, improve landing page performance, and prevent churn from happening.

Kaggle provides a Jupyter Notebooks environment that is customizable and easy to set up. You can access free GPUs and a large repository of community-published data & codes. Kaggle contains all the code and data you need for data science. You can conquer any analysis with over 19,000 public datasets, and 200,000 public notebooks.

A simple-to-use interactive tool that allows data exploration, analysis, visualization and machine learning. Cloud Datalab is an interactive tool that allows you to analyze, transform, visualize, and create machine learning models on Google Cloud Platform. It runs on Compute Engine. It connects to multiple cloud services quickly so you can concentrate on data science tasks. Cloud Datalab is built using Jupyter (formerly IPython), a platform that boasts a rich ecosystem of modules and a solid knowledge base. Cloud Datalab allows you to analyze your data on BigQuery and AI Platform, Compute Engine and Cloud Storage using Python and SQL. JavaScript is also available (for BigQuery user defined functions). Cloud Datalab can handle megabytes and terabytes of data. Cloud Datalab allows you to query terabytes and run local analysis on samples of data, as well as run training jobs on terabytes in AI Platform.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

All the Python tools in one location. PyCharm will take care of the routine, saving you time. To make the most of PyCharm's productivity features, you should focus on the important things. PyCharm has all the information you need about your code. PyCharm can help you with intelligent code completion, quick error checking and quick fixes, project navigation, and many other things. The IDE allows you to write clean and maintainable code and helps you maintain control of quality with PEP8 tests, testing assistance and smart refactorings. PyCharm was created by programmers for programmers to give you all the tools you need to create Python code. PyCharm offers smart code completion, code inspections and quick-fixes. It also includes automated code refactorings.

Colaboratory, also known as "Colab", allows you to create and execute Python from your browser using the web browser. - Zero configuration required Free access to GPUs Easy sharing Colab is available to all levels of the AI research community, including students, data scientists, and researchers. Colab notebooks enable you to combine executable and rich text into one document. They also include images, HTML, LaTeX and more. Your Google Drive account stores your Colab notebooks. Your Colab notebooks can be shared with friends and coworkers. They can be edited or commented on by them.

R Markdown documents can be reproduced. You can use a productive notebook interface for combining code and narrative text to create elegantly formatted output. Multiple languages are supported, including R, Python, SQL. R Markdown supports many different output formats, including HTML, PDF and MS Word. R Markdown is a data science authoring framework. To use both, you can use one R Markdown file. The file opens in the RStudio IDE and becomes a notebook interface to R. Each code chunk can be run by clicking the icon. RStudio executes your code and displays the results inline with the file.

Org is implemented on top Of Outline mode. This makes it possible to keep large files well-structured. The tree can be used for structure editing and visibility cycling. A built-in table editor makes it easy to create tables. Plain text URL-like links link to websites, emails and Usenet messages. Org creates organizational tasks around notes files that contain plain text information or lists about projects. Metadata is part of an outline node and is used for task management and project planning. This data can be used to extract specific entries and create dynamic agenda views. These views also include the Emacs diary and calendar. Org can be used for many different project planning schemes such as David Allen's GTD. Org files can be used as a single source authoring tool with export to many formats, such as HTML, LaTeX and Open Document.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Streamlit. The fastest way to create and share data apps. In minutes, turn data scripts into sharable Web apps All in Python. All this for free. No need for front-end experience. Streamlit combines three simple concepts. Use Python scripting. Our API is simple and allows you to create an app in just a few lines of code. You can then see the app update automatically as you save your source file. You can also use interaction. Declaring a variable is the same thing as adding a widget. You don't need to create a backend, define routes or handle HTTP requests. You can deploy your app instantly. Streamlit's platform for sharing allows you to easily share, manage and collaborate on your apps. A framework that allows you to create powerful apps. Face-GAN explorer. App that generates faces matching selected attributes using Shaobo Guan’s TL-GAN project, TensorFlow and NVIDIA’s PG-GAN. Real time object detection. A browser that displays images from the Udacity self driving-car dataset.

Project Jupyter is an open-source project that develops open-standards software and services for interactive computing in dozens of programming languages. JupyterLab provides a web-based interactive environment for Jupyter notebooks and code. JupyterLab's user interface is flexible. You can configure and arrange it to support a variety of workflows in data science and scientific computing. JupyterLab can be extended and modified to add new components or integrate with existing ones. Open-source web application, Jupyter Notebook, allows you to create and share documents with live code, equations and visualizations. Data cleaning and transformation, numerical modeling, statistical modeling and data visualization are just a few of the many uses. Jupyter supports more than 40 programming languages, including Python and R, Julia, Scala, and Scala.

IBM Analytics for Apache Spark allows data scientists to ask more difficult questions and deliver business value quicker with a flexible, integrated Spark service. It's a simple-to-use, managed service that is always on and doesn't require any long-term commitment. You can start exploring immediately. You can access the power of Apache Spark without locking yourself in, thanks to IBM's open-source commitment as well as decades of enterprise experience. With Notebooks as a connector, coding and analytics are faster and easier with managed Spark services. This allows you to spend more time on innovation and delivery. You can access the power of machine learning libraries through managed Apache Spark services without having to manage a Sparkcluster by yourself.

Access, explore, and prepare data while discovering new patterns and trends. SAS Visual Data Science allows you to create and share interactive visualizations and reports using a single interface. It uses machine learning, text analysis, and econometrics to improve forecasting and optimization. Additionally, it registers SAS and open source models within projects and as standalone models. Visualize your data and find relevant relationships. You can create and share interactive dashboards and reports, and use self service analytics to quickly assess possible outcomes for better, data-driven decisions. This solution runs in SAS®, Viya®. It allows you to explore data and create or adjust predictive analytical models. Analysts, statisticians, data scientists, and analysts can work together to refine and refine models for each group or segment, allowing them to make informed decisions.

Easily extract, transform, manage & process CAD data, FE data, CFD and test results. Create automatic data pipelines to support machine learning, deep learning, and ROM. Data science barriers can be removed without coding. Key Ward's platform, the first engineering no-code end-to-end solution, redefines how engineers work with their data. Our software allows engineers to handle multi-source data with ease, extract direct value using our built-in advanced analytical tools, and build custom machine and deep learning model with just a few clicks. Automatically centralize, update and extract your multi-source data, then sort, clean and prepare it for analysis, machine and/or deep learning. Use our advanced analytics tools to correlate, identify patterns, and find dependencies in your experimental & simulator data.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

MATLAB®, a combination of a desktop environment for iterative analysis, design processes, and a programming language that expresses matrix or array mathematics directly, is MATLAB®. It also includes the Live Editor, which allows you to create scripts that combine output, code, and formatted text in an executable notebook. MATLAB toolboxes have been professionally developed, tested and documented. MATLAB apps allow you to see how different algorithms interact with your data. You can repeat the process until you get the results you desire. Then, MATLAB will automatically generate a program to replicate or automate your work. With minor code changes, you can scale your analyses to run on GPUs, clusters, and clouds. You don't need to rewrite any code or learn big-data programming and other out-of-memory methods. Convert MATLAB algorithms automatically to C/C++ and HDL to run on your embedded processor/FPGA/ASIC. Simulink works with MATLAB to support Model-Based Design.

A fully-featured machine learning platform empowers enterprises to conduct real data science at scale and speed. You can spend less time managing infrastructure and tools so that you can concentrate on building machine learning applications to propel your business forward. Anaconda Enterprise removes the hassle from ML operations and puts open-source innovation at the fingertips. It provides the foundation for serious machine learning and data science production without locking you into any specific models, templates, workflows, or models. AE allows data scientists and software developers to work together to create, test, debug and deploy models using their preferred languages. AE gives developers and data scientists access to both notebooks as well as IDEs, allowing them to work more efficiently together. They can also choose between preconfigured projects and example projects. AE projects can be easily moved from one environment to the next by being automatically packaged.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

SAS®, Viya®, data science offerings offer a comprehensive, scalable analytical environment that is quick and easy to use, allowing you to meet diverse business requirements. Automatically generated insights allow you to identify the most commonly used variables across all models, the most significant variables selected across models, and assess results for all models. Natural language generation capabilities allow you to create project summaries in plain language. This makes it easy to interpret reports. Analytics team members can add project notes and comments to the insights report to facilitate communication between team members. SAS allows you to embed open source code into an analysis and call open-source algorithms seamlessly within its environment. This allows for collaboration within your organization as users can program in the language they prefer. SAS Deep Learning with Python (DLPy) is also available on GitHub.

Machine Learning and Predictive Analytics Platform. Advanced artificial intelligence is a pre-built platform for machine learning and big-data analytics that can enhance data-driven decision making. OpenText Magellan makes predictive analytics easy to use and provides flexible data visualizations that maximize business intelligence. Artificial intelligence software reduces the need to manually process large amounts of data. It presents valuable business insights in a manner that is easily accessible and relevant to the organization's most important objectives. Organizations can enhance business processes by using a curated combination of capabilities such as predictive modeling, data discovery tools and data mining techniques. IoT data analytics is another way to use data to improve decision-making based on real business intelligence.

Datalore is a platform for collaborative data science and analytics that aims to improve the entire analytics workflow and make working with data more enjoyable for both data scientists as well as data-savvy business teams. Datalore is a collaborative platform that focuses on data teams workflow. It offers technical-savvy business users the opportunity to work with data teams using no-code and low-code, as well as the power of Jupyter Notebooks. Datalore allows business users to perform analytic self-service. They can work with data using SQL or no-code cells, create reports, and dive deep into data. It allows core data teams to focus on simpler tasks. Datalore allows data scientists and analysts to share their results with ML Engineers. You can share your code with ML Engineers on powerful CPUs and GPUs, and you can collaborate with your colleagues in real time.

Manage, control, secure, and manage the apps, data, and IT that run your business from edge to cloud. HPE Ezmeral accelerates digital transformation initiatives by shifting resources and time from IT operations to innovation. Modernize your apps. Simplify your operations. You can harness data to transform insights into impact. Kubernetes can be deployed at scale in your data center or on the edge. It integrates persistent data storage to allow app modernization on baremetal or VMs. This will accelerate time-to-value. Operationalizing the entire process to build data pipelines will allow you to harness data faster and gain insights. DevOps agility is key to machine learning's lifecycle. This will enable you to deliver a unified data network. Automation and advanced artificial intelligence can increase efficiency and agility in IT Ops. Provide security and control to reduce risk and lower costs. The HPE Ezmeral Container Platform is an enterprise-grade platform that deploys Kubernetes at large scale for a wide variety of uses.

Integrate analytics into real time interactions and event-based capabilities. SAS Visual Data Science Decisioning offers robust data management, visualization, advanced analysis, and model management. It supports decision making by creating, embedding, and governing analytically driven decision flows at scale in batch or real-time. It also provides analytics and stream-based decisions to help you uncover insights. A comprehensive visual interface allows you to solve complex analytical problems. It handles all aspects of the analytics lifecycle. SAS Visual Data Mining and Machine Learning runs in SAS®, Viya®. It combines data wrangling and exploration with feature engineering and modern statistical, data mining and machine learning techniques in one, scalable, in-memory processing environment. This web application is a development tool that you can access via your browser.

SAS Visual Statistics allows multiple users to interactively explore data and then create and refine predictive models. Your statisticians and data scientists can use the most appropriate analytical modeling techniques to analyze your observations at a fine level. What will you get? The result? You can quickly build and refine models to target specific segments or groups, and run multiple scenarios simultaneously. To get better results, you can ask more "what-if" questions. You can also use an automatically generated score code to put your results into practice. Multiple users can interact with data visually. They can add, change, or remove outliers. You can instantly see how changes affect the predictive power of your model and make adjustments quickly. Data science teams have the freedom to work in the language they prefer, so they can make the most of their talents. SAS Visual Statistics combines all analytical assets.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

Data scientists are able to build, improve, or operate end-to–end workflows independently. This allows them to deliver data science projects that are successful. Metaflow can be used with your favorite data science libraries such as SciKit Learn or Tensorflow. You can write your models in idiomatic Python codes with little to no learning. Metaflow also supports R language. Metaflow allows you to design your workflow, scale it, and then deploy it to production. It automatically tracks and versions all your data and experiments. It allows you to easily inspect the results in notebooks. Metaflow comes pre-installed with the tutorials so it's easy to get started. Metaflow allows you to make duplicates of all tutorials in your current directory by using the command line interface.

Metacoder makes data processing faster and more efficient. Metacoder provides data analysts with the flexibility and tools they need to make data analysis easier. Metacoder automates data preparation steps like cleaning, reducing the time it takes to inspect your data before you can get up and running. It is a good company when compared to other companies. Metacoder is cheaper than similar companies and our management is actively developing based upon our valued customers' feedback. Metacoder is primarily used to support predictive analytics professionals in their work. We offer interfaces for database integrations, data cleaning, preprocessing, modeling, and display/interpretation of results. We make it easy to manage the machine learning pipeline and help organizations share their work. Soon, we will offer code-free solutions for image, audio and video as well as biomedical data.

IBM SPSS Modeler, a leading visual data-science and machine-learning (ML) solution, is designed to help enterprises accelerate their time to value through the automation of operational tasks by data scientists. It is used by organizations around the world for data preparation, discovery, predictive analytics and model management and deployment. ML is also used to monetize data assets. IBM SPSS Modeler transforms data in the best possible format for accurate predictive modeling. You can now analyze data in just a few clicks, identify fixes, screen fields out and derive new characteristics. IBM SPSS Modeler uses its powerful graphics engine to help you bring your insights to life. The smart chart recommender will select the best chart from dozens of options to share your insights.

Predictive modeling with Machine Learning and Explainable Ai. FICO®, Analytics Workbench™, is a comprehensive suite of state-of the-art analytic authoring software that empowers companies to make better business decisions throughout the customer lifecycle. Data scientists can use it to build superior decisioning abilities using a variety of predictive data modeling tools, including the most recent machine learning (ML), and explainable AI (xAI) methods. FICO's innovative intellectual property enables us to combine the best of open-source data science and machine learning to provide world-class analytical capabilities to find, combine, and operationalize data predictive signals. Analytics Workbench is built upon the FICO®, leading platform that allows for new predictive models and strategies to easily be put into production.

Snowflake®, a cloud data platform and enterprise-grade architecture, allows you to securely store and use your data in the cloud. Drag-and-drop visual workflow design and built-in ETL to connect, clean and transform data from 250+ sources. You can generate actionable insights and insights from your data using the latest Search and AI-driven technology. Our AI/ML environments can be used to build, tune, and deploy models for predictive analytics or forecasting. Our AI/ML environments are available to help you take your data to new heights. The PurpleCube Data Science module allows you to create, train, tune, and deploy AI models for forecasting and predictive analysis. PurpleCube Analytics allows you to create BI visualizations, search your data with natural language and use AI-driven insights and smart recommendations to provide answers to questions that you didn't know to ask.

H2O.ai, the open-source leader in AI and machinelearning, has a mission to democratize AI. Our enterprise-ready platforms, which are industry-leading, are used by thousands of data scientists from over 20,000 organizations worldwide. Every company can become an AI company in financial, insurance, healthcare and retail. We also empower them to deliver real value and transform businesses.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

HyperCube is the platform that data scientists use to quickly discover hidden insights, no matter what your business needs. Use your business data to make an impact. Unlock understanding, uncover untapped opportunities, make predictions, and avoid risk before they happen. HyperCube turns huge amounts of data into actionable insights. HyperCube is for you, whether you are a beginner or an expert in machine learning. It is the data science Swiss Army knife. It combines proprietary and open-source code to deliver a wide variety of data analysis features right out of the box. Or, it can be customized for your business. We are constantly improving our technology to deliver the best possible results. Choose from apps, DaaS (data-as-a service) or vertical market solutions.

Quadratic allows your team to collaborate on data analysis and deliver faster results. You're already familiar with spreadsheets, but this is the first time you have had so much power. Quadratic is fluent in Formulas, Python and SQL (JavaScript & SQL coming soon). Use the language that you and your team are already familiar with. Single-line formulas can be difficult to read. Quadratic allows you to expand your recipes as many times as you want. Quadratic comes with Python library support. Bring the latest open source tools to your spreadsheet. The last line of the code is returned to your spreadsheet. By default, raw values, 1/2D arrays and Pandas DataFrames can be used. Quadratic updates its cells automatically when data is pulled or fetched from an external API. Zoom out to see the big picture and zoom in for the details. Arrange your data and navigate it the way you see it in your mind, not as a tool would have it.

Data science platform that increases productivity and has unparalleled capabilities. Create and evaluate machine learning (ML), models of higher quality. Easy deployment of ML models can help increase business flexibility and enable enterprise-trusted data work faster. Cloud-based platforms can be used to uncover new business insights. Iterative processes are necessary to build a machine-learning model. This ebook will explain how machine learning models are constructed and break down the process. Use notebooks to build and test machine learning algorithms. AutoML will show you the results of data science. It is easier and faster to create high-quality models. Automated machine-learning capabilities quickly analyze the data and recommend the best data features and algorithms. Automated machine learning also tunes the model and explains its results.

Easy extract, transform, load (ETL), data for data science or analytics. Code-free data flows can be created into data lakes or data marts. Oracle's extensive portfolio of integration solutions. The intuitive user interface allows you to set up integration parameters and automate data mapping from sources and targets. To shape your data, you can use one of the many out-of-the box operators such as a join or aggregate. You can centrally manage your processes and use parameters to override certain configuration values at runtime. Users can view and interact with their data to validate their processes. You can increase productivity and fine-tune data flow on the fly without waiting for executions to complete. Reduce maintenance complexity and avoid broken integration flows as data schemas change.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

All work can be synced, searched and managed across your data science team. Zepl's powerful search allows you to discover and reuse models, code, and other data. Zepl's enterprise collaboration platform allows you to query data from Snowflake or Athena and then build your models in Python. For enhanced interactions with your data, use dynamic forms and pivoting. Zepl creates new containers every time you open your notebook. This ensures that you have the same image each time your models are run. You can invite your team members to join you in a shared space, and they will be able to work together in real-time. Or they can simply leave comments on a notebook. You can share your work with fine-grained access controls. You can allow others to read, edit, run, and share your work. This will facilitate collaboration and distribution. All notebooks can be saved and versioned automatically. An easy-to-use interface allows you to name, manage, roll back, and roll back all versions. You can also export seamlessly into Github.