Alternatives to marimo

Codeium is the modern code superpower. It's a free AI-powered code acceleration toolkit. Codeium currently provides AI-generated autocomplete in more than 20 programming languages (including Python and JS, Java, TS, Java and Go) and integrates directly to the developer's IDE (VSCode, JetBrains or Jupyter notebooks. Colab, Vim / Neoovim, etc. Codeium generates multiline code suggestions in a matter of seconds. This will eliminate the need to search for APIs and documentation, write boilerplate and unit test scripts, and many other tedious or frustrating tasks. Codeium is a training platform that allows you to quickly develop on billions of lines. It also helps you stay in the flow and lets you become the best coder you can be.

Bokeh allows you to create simple plots but can also handle custom or specialized use cases. Apps, dashboards and plots can be published on web pages or in Jupyter notebooks. Python offers a wide range of powerful analytics tools, including NumPy and Scipy, Pandas. Scikit-Learn, OpenCV and Scikit-Learn. Bokeh server allows you to connect these tools to rich interactive visualizations in your browser. It has a wide range of widgets, plot tools and UI events that trigger Python callbacks. Researchers at Monash University maintain Microscopium. Researchers can use it to explore large image datasets using Bokeh's interactive tools. Panel is a tool that allows for polished data presentation and makes use of the Bokeh server. Anaconda created it and supports it. Panel allows you to easily create interactive web apps and dashboards using user-definable widgets. These widgets can be connected to plots, images or tables, text, or any other data.

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.

BeakerX is a collection kernels and extensions for the Jupyter interactive computing system. It supports Spark cluster support, JVM support and polyglot programming. Interactive plots, tables, forms, publishing, as well as JVM support are all available. All JVM languages of BeakerX, including JavaScript, have APIs that allow interactive time-series and scatter plots, histograms and heatmaps. Both notebooks that have been saved to disk and notebooks that are published to the internet retain the widgets' interactive nature. They have unique features that allow you to handle many points, zooming in and out, as well as zooming in at nanosecond resolution. The table widget by BeakerX automatically recognizes pandas data frames. It allows you to search and sort, drag, filter and format, select, format, select and graph, hide, pin and export to CSV and clipboard. This makes it easy to connect to spreadsheets. Spark magic is available in BeakerX. It includes GUIs that allow you to configure, monitor, progress, and interrupt Spark jobs. You can either use the GUI, or create your SparkSession using code.

You can instantly start coding with an interactive Jupyter notebook that runs on the cloud. There is no installation or setup. You can start with a blank notebook. Follow the tutorials or use a starter template. Jovian allows you to manage all your projects. To capture snapshots and record versions, run jovian.commit(). This will generate shareable links to your notebooks. Your best work can be displayed on your Jovian profile. You can feature projects, notebooks and collections, as well as activities. With simple, intuitive and visual notebook diffs, you can track code changes, outputs, graphs and tables, logs, and much more. You can share your work online or privately with your team. You can let others help you build on your work and contribute back. With a powerful cell-level commenting interface, collaborators can discuss and comment on particular parts of your notebooks. Flexible comparison dashboard allows you to sort, filter, archive, and do much more to analyze ML results.

Count is an interactive data whiteboard that allows for full collaboration. It combines the flexibility and creativity that a whiteboard offers with the power and reactivity of BI Notebooks. It is easy to break down complex SQL queries and data model into interconnected cells for better understanding the logic. Use sticky notes and graphics to help stakeholders understand your work. As you build, you can collaborate with other analysts or stakeholders to get faster feedback. Turn any canvas into a slideshow or interactive report.

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.

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.

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.

CVXOPT, a free software package that allows convex optimization using the Python programming language, is available for download. It can be used with Python's interactive interpreter, the command line to execute Python scripts or integrated into other software via Python extension module. Its primary purpose is to simplify the development of software for convex optimization by using Python's extensive standard library as well as the strengths of Python, a high-level programming languages. Efficient Python classes that can handle dense and sparse (real and complicated) matrices. Includes Python indexing, slicing, and overloaded operations for matrix mathmetic. Interfaces to the linear programming solution in GLPK, semidefinite programming solvers DSDP5 and linear, quadratic, and second-order cone programming solutions in MOSEK.

Atlas integrates with your workflow by organizing text, embedding datasets and creating interactive maps that can be explored in a web browser. To understand your data, you don't need to scroll through Excel files or log Dataframes. Atlas automatically analyzes, organizes, and summarizes your documents, surfacing patterns and trends. Atlas' pre-organized data interface makes it easy to quickly identify and remove any data that could be harmful to your AI projects. You can label and tag your data, while cleaning it up with instant sync to your Jupyter notebook. Although vector databases are powerful, they can be difficult to interpret. Atlas stores, visualizes, and allows you to search through all your vectors within the same API.

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.

Hex combines the best of notebooks and BI into a seamless, collaborative interface. Hex is a modern Data Workspace. It makes it easy for you to connect to data and analyze it in collaborative SQL or Python-powered notebooks. You can also share work as interactive data apps or stories. The Projects page is your default landing page in Hex. You can quickly find the projects you have created and those you share with others. The outline gives you an easy-to-read overview of all cells in a project's Logic View. Each cell in the outline lists all variables it defines and any cells that return an output (chart cells or Input Parameters cells, etc.). Display a preview of the output. To jump to a specific position in the logic, you can click on any cell in the outline.

Collimator is a simulation and modeling platform for hybrid dynamical system. Engineers can design and test complex, mission-critical systems in a reliable, secure, fast, and intuitive way with Collimator. Our customers are control system engineers from the electrical, mechanical, and control sectors. They use Collimator to improve productivity, performance, and collaborate more effectively. Our out-of-the-box features include an intuitive block diagram editor, Python blocks for developing custom algorithms, Jupyter notebooks for optimizing their systems, high performance computing in cloud, and role-based access controls.

The last cron manager that you will ever require Zero infra deployment on time of Python scripts generated by humans or AI. STEPS: 1. Bring your own code (BYOC): If your code is already ready in VS Code, Jupyter Notebook or any other code editor, you can paste it directly on Xq1. ChatGPT can generate your code with a simple "Write a Python code for ....", and paste it on the Xq1 2. Run your code: Run your code on Xq1. This will deploy any packages you have included in the code, create a container and run the container. If the code runs smoothly, you can proceed. 3. Select the schedule you want to use. Select the schedule or frequency you wish to run your code. 4. Press the deploy button. Xq1 can deploy your container and schedule it for the frequency or schedule you choose. You can track each run using the 'Cron Monitor UI' on Xq1.

Machine Learning that is ready for production is commoditized. ZenML, the most popular MAIOT product, allows you to build reproducible Machine Learning pipelines using an extensible, open source MLOps framework. ZenML pipelines can be used to perform experiments, from data versioning to the deployment of a model. The core design is built around flexible interfaces that can accommodate complex pipeline scenarios. It also provides a simple, battery-included "happy path" to success in common use cases without any boilerplate code. Data Scientists should be able to focus on the use-cases, goals, and ultimately, Machine Learning workflows, and not the underlying technologies. We want to help Machine Learning professionals adopt new technologies as quickly as possible, as both the Software and Hardware landscapes are changing rapidly. To do this, we will decouple reproducible workflows that can be used to produce Machine Learning from the required tools.

PySpark is a Python interface for Apache Spark. It allows you to create Spark applications using Python APIs. Additionally, it provides the PySpark shell that allows you to interactively analyze your data in a distributed environment. PySpark supports Spark's most popular features, including Spark SQL, DataFrame and Streaming. Spark SQL is a Spark module that allows structured data processing. It can be used as a distributed SQL query engine and a programming abstraction called DataFrame. The streaming feature in Apache Spark, which runs on top of Spark allows for powerful interactive and analytic applications across streaming and historical data. It also inherits Spark's ease-of-use and fault tolerance characteristics.

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.

Data-driven computational pipelines. Nextflow allows for reproducible and scalable scientific workflows by using software containers. It allows adaptation of scripts written in most common scripting languages. Fluent DSL makes it easy to implement and deploy complex reactive and parallel workflows on clusters and clouds. Nextflow was built on the belief that Linux is the lingua Franca of data science. Nextflow makes it easier to create a computational pipeline that can be used to combine many tasks. You can reuse existing scripts and tools. Additionally, you don't have to learn a new language to use Nextflow. Nextflow supports Docker, Singularity and other containers technology. This, together with integration of the GitHub Code-sharing Platform, allows you write self-contained pipes, manage versions, reproduce any configuration quickly, and allow you to integrate the GitHub code-sharing portal. Nextflow acts as an abstraction layer between the logic of your pipeline and its execution layer.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Polars, which is aware of the data-wrangling habits of its users, exposes a complete Python interface, including all of the features necessary to manipulate DataFrames. This includes an expression language, which will allow you to write readable, performant code. Polars was written in Rust to provide the Rust ecosystem with a feature-complete DataFrame interface. Use it as either a DataFrame Library or as a query backend for your Data Models.

Choose two custom cell groups and compare their top differentially-expressed genes. Use millions of cells in the integrated CZ CELLxGENE Corpus for powerful analyses. Use an interactive, no-code interface to perform interactive analyses of a dataset. Explore how spatial, environmental and genetic factors influence gene expression patterns. Use published datasets to understand them or as a starting point for identifying new cell subtypes and states. Census allows you to access any custom slice of standard cell data from CZ CELLxGENE in R or Python. Explore an interactive encyclopedia that contains 700+ cell types, detailed definitions, markers genes, lineage and relevant datasets. Browse and download 1,000+ datasets and hundreds of standardized data sets that characterize the functionality of healthy human and mouse tissues.

A platform for machine learning and deep learning applications that is reproducible and scaleable. Learn more about the products and features that make up today's most innovative platform to manage data science workflows. Polyaxon offers an interactive workspace that includes notebooks, tensorboards and visualizations. You can collaborate with your team and share and compare results. Reproducible results are possible with the built-in version control system for code and experiments. Polyaxon can be deployed on-premises, in the cloud, or in hybrid environments. This includes single laptops, container management platforms, and Kubernetes. You can spin up or down, add nodes, increase storage, and add more GPUs.

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.

It works with Jupyter Notebooks or any other Python environment. Modelbit will deploy your model and all its dependencies to production by calling modelbi.deploy. Modelbit's ML models can be called from your warehouse just as easily as a SQL function. They can be called directly as a REST-endpoint from your product. Modelbit is backed up by your git repository. GitHub, GitLab or your own. Code review. CI/CD pipelines. PRs and merge request. Bring your entire git workflow into your Python ML models. Modelbit integrates seamlessly into Hex, DeepNote and Noteable. Modelbit lets you take your model directly from your cloud notebook to production. Tired of VPC configurations or IAM roles? Redeploy SageMaker models seamlessly to Modelbit. Modelbit's platform is available to you immediately with the models that you have already created.

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.

CData Python Connectors make it easy for Python users to connect to SaaS and Big Data, NoSQL and relational data sources. Our Python Connectors provide simple Python database interfaces to (DB-API), making them easy to connect to popular tools like Jupyter Notebook and SQLAlchemy. CData Python Connectors wrap SQL around APIs and data protocol, making it easier to access data from Python. It also allows Python users to connect more than 150 SaaS and Big Data data sources with advanced Python processing. The CData Python Connectors bridge a critical gap in Python tooling, providing consistent connectivity with data-centric interfaces for hundreds of SaaS/Cloud, NoSQL and Big Data sources. Download a 30-day free trial or learn more at: https://www.cdata.com/python/

Tellurium is an important Python package for conducting simulation studies in other disciplines and systems biology. Tellurium is an interface for the powerful, high-performance lib Roadrunner simulation engine. Tellurium lets you build your models with an easy-to use human-readable version SBML called Antimony. Antimony Tutorial. Tellurium supports all major standards, such as SBML and SED-ML archives. Tellurium is available via GUI front-ends like Spyder, PyCharm or Jupyter Notebooks, with support for advanced productivity features and interactive editing. Installation is done via standard pip. We also offer a Windows one-click installer that provides a complete system biology modeling environment. Tellurium relies heavily on contributions from open-source.

Hopsworks is an open source Enterprise platform that allows you to develop and operate Machine Learning (ML), pipelines at scale. It is built around the first Feature Store for ML in the industry. You can quickly move from data exploration and model building in Python with Jupyter notebooks. Conda is all you need to run production-quality end-to-end ML pipes. Hopsworks can access data from any datasources you choose. They can be in the cloud, on premise, IoT networks or from your Industry 4.0-solution. You can deploy on-premises using your hardware or your preferred cloud provider. Hopsworks will offer the same user experience in cloud deployments or the most secure air-gapped deployments.

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.

IronPython, an open-source implementation for the Python programming language, is tightly integrated with.NET. IronPython is compatible with.NET and Python libraries. Other.NET languages can also use Python code. With Python Tools for Visual Studio, you can have a more interactive.NET/Python development experience. IronPython, a great addition to.NET that gives Python developers the power of.NET, is a great addition to.NET. IronPython is also available to existing.NET developers. It can be used as a fast, expressive scripting language for writing, testing, and embedding new applications. The CLR is an excellent platform for creating programming languages. The DLR makes it even better for dynamic languages. The.NET (base library, presentation foundation, and so on) is a great tool for developers. Developers have access to a lot of functionality and power through the.NET (base class library, presentation foundation, etc.) IronPython uses Python syntax, standard libraries, and your Python code will need updating accordingly.

StarCoderBase and StarCoder are Large Language Models (Code LLMs), trained on permissively-licensed data from GitHub. This includes data from 80+ programming language, Git commits and issues, Jupyter Notebooks, and Git commits. We trained a 15B-parameter model for 1 trillion tokens, similar to LLaMA. We refined the StarCoderBase for 35B Python tokens. The result is a new model we call StarCoder. StarCoderBase is a model that outperforms other open Code LLMs in popular programming benchmarks. It also matches or exceeds closed models like code-cushman001 from OpenAI, the original Codex model which powered early versions GitHub Copilot. StarCoder models are able to process more input with a context length over 8,000 tokens than any other open LLM. This allows for a variety of interesting applications. By prompting the StarCoder model with a series dialogues, we allowed them to act like a technical assistant.

Our powerful algorithms allow you to add complexity to your models to better represent reality, while still solving your model in the time available. Gurobi can be easily integrated into your applications using the languages that you are most familiar with. Our programming interfaces have been designed to be intuitive, lightweight, and modern to reduce your learning curve, while maximising your productivity. Our Python API features higher-level modeling constructs to make it easier to create optimization models. Choose from Anaconda Python Distributions that include pre-built libraries for application development. Spyder is for graphical development and Jupyter is for notebook-style development.

Visual builder that requires little code to create dynamic, data-driven and interactive custom web sites, mobile apps, and CMS systems. It integrates first-class reactive front-end and back end frameworks, visual data bindings, Bootstrap 4 builder, and visual two-way data bindings. This makes it easy to focus on productivity and creativity. Wappler allows for you to connect to any API service or database. Wappler's Docker integration allows you to not only develop locally but also deploy to remote Docker hosts. Git integration allows for full version control.

Kubeflow is a project that makes machine learning (ML), workflows on Kubernetes portable, scalable, and easy to deploy. Our goal is not create new services, but to make it easy to deploy the best-of-breed open source systems for ML to different infrastructures. Kubeflow can be run anywhere Kubernetes is running. Kubeflow offers a custom TensorFlow job operator that can be used to train your ML model. Kubeflow's job manager can handle distributed TensorFlow training jobs. You can configure the training controller to use GPUs or CPUs, and to adapt to different cluster sizes. Kubeflow provides services to create and manage interactive Jupyter Notebooks. You can adjust your notebook deployment and compute resources to meet your data science requirements. You can experiment with your workflows locally and then move them to the cloud when you are ready.

WebAssembly, also known as Wasm, is a binary instruction format that can be used to build a virtual machine using a stack-based architecture. Wasm is a portable compilation target for programming language, which allows deployment on the internet for client and server applications. The Wasm stack machine was designed to be encoded using a small and efficient binary format. WebAssembly is designed to run natively on a wide variety of platforms, taking advantage of common hardware capabilities. WebAssembly describes a memory safe, sandboxed execution system that can even be implemented within existing JavaScript virtual machines. WebAssembly, embedded in the web will enforce the same-origin security policies as the browser. WebAssembly was designed to be printed in a textual format. This allows for debugging and testing, optimizing, optimizing, optimizing, learning, teaching, writing programs by hand, and experimenting. This textual format is used to view the Wasm modules' source on the internet.

RPA platform that is powerful and affordable. Use Python's flexibility, low-code's convenience, and AI to automate intelligently. Python RPA is a simple-to-use platform that allows you to create and manage bots in Python. Python's capabilities make it a powerful and effective tool for automating business process. Orchestrator enterprise-grade for managing Python scripts, low-code projects. You only need a basic understanding of Python to begin your automation journey. Instant notifications and a status board will keep you in the loop. Ensure smooth, uninterrupted process execution. Managed and secured user access. Secure your credentials and make sure that all activities are logged. Create your project using any library or framework. You can use any open-source Python environment to develop your Python automation.

Livewire is an all-in-one framework for Laravel. It simplifies the creation of dynamic interfaces, without having to leave Laravel. It allows developers create modern, responsive web applications using Laravel’s Blade templating engines, eliminating the requirement for a separate framework. Livewire components are able to communicate with one another through a global events system, allowing seamless interaction between components. The framework provides features such as data binding, validation and lifecycle hooks to facilitate the development of dynamic interfaces. Livewire simplifies dynamic UIs by handling frontend interactions at the server level. Developers can focus on application logic instead of JavaScript. Livewire renders initial component output along with the page. It's SEO friendly. Livewire sends an AJAX request with the updated data to the server when an interaction occurs.

Nim is a statically-typed compiled systems programming languages. It combines the best concepts of mature languages such as Ada, Python, and Modula. Nim generates native dependencies-free executables that are not dependent on a virtual computer. They are small and can be redistributed easily. Nim's memory management, which is deterministic and customizable, has destructors and moves semantics that are inspired by C++/Rust. It is well-suited to embedded, hard-realtime applications. Modern concepts such as zero-overhead iterators, compile-time evaluations of user-defined functions, and the preference for value-based datatypes on the stack make code extremely performant. It supports multiple backends: it can compile to C, C++, JavaScript so that Nim is available for all frontend and backend needs.

Tecplot 360 is the most comprehensive CFD Post processor. Make better decisions with Tecplot 360. More CFD simulations are being performed, grid sizes are increasing, and data sets can be stored remotely. It is essential to have the right tools to manage large data sets, automate workflows, visualize parametric results, and visualize them. Tecplot 360 allows you to spend less time waiting and more time exploring. Integrate XY, 2D and 3D plots to get them exactly how you want. Brilliant images and animations can help you communicate your results. PyTecplot Python scripting automates the tedious stuff. Chorus makes it easy to analyze parametric data. Securely access remote data via the SZL-Server client/server. Load Tecplot and FLUENT. Multi-frame environments with multiple pages can be used to report and compare solutions.

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.

Open source data governance suite to manage data lakes and databases. Tokern is an easy-to-use toolkit for collecting, organizing and analysing metadata from data lakes. Runs as a command-line application for quick tasks. Run as a service to continuously collect metadata. Use reporting dashboards to analyze lineage, access control, and PII data. Or programmatically in Jupyter notebooks. Tokern is an open-source data governance suite for data lakes and databases. You can improve the ROI of your data, comply to regulations like HIPAA, CCPA, and GDPR, and protect your data from insider threats with confidence. Centralized metadata management for users, jobs, and datasets. Other data governance features are powered by this feature. Track column-level data lineage for Snowflake and AWS Redshift. You can build lineage using query history or ETL scripts. Interactive graphs and programming with APIs and SDKs allow you to explore lineage.

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.

Aurelia's unobtrusive, standards-based style makes it the only framework that allows you to create components using vanilla JavaScript and TypeScript. There is little to no additional information required to create complex apps. Aurelia's core is a reactive, high-performance system that can batch DOM updates. This makes it stand out from other frameworks and their virtual DOMs. No matter how complex your interface is, you will experience consistent and scalable performance. Aurelia allows you to easily react to any object. Aurelia uses adaptive techniques to determine the most efficient way for you to observe each property of your model. It automatically syncs your state and your UI with best-in class performance. All official plugins of the core team for state management, internationalization, and validation. CLI, VS Code plugin and Chrome debugger – optional tools to improve development.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

esDynamic allows you to maximize your security testing journey. From setting up your bench, to analyzing the results of your data processing, esDynamic will save you time and effort. Discover the comprehensive and flexible Python-based platform that is perfect for every phase of security analysis. You can easily customize your research space by adding new equipment, integrating different tools, and changing data. esDynamic also offers a large collection of materials that cover complex topics, which would normally require extensive research and a team of experts. This gives you instant access to expertise. Say goodbye to fragmented data and scattered knowledge. Welcome to a cohesive workspace that allows your team to easily share data and insights. This will foster collaboration and accelerate discoveries. Share your JupyterLab Notebooks with your team to centralize and solidify all your efforts.

Daft is an ETL, analytics, and ML/AI framework that can be used at scale. Its familiar Python Dataframe API is designed to outperform Spark both in terms of performance and ease-of-use. Daft integrates directly with your ML/AI platform through zero-copy integrations of essential Python libraries, such as Pytorch or Ray. It also allows GPUs to be requested as a resource when running models. Daft is a lightweight, multithreaded local backend. When your local machine becomes insufficient, it can scale seamlessly to run on a distributed cluster. Daft supports User-Defined Functions in columns. This allows you to apply complex operations and expressions to Python objects, with the flexibility required for ML/AI. Daft is a lightweight, multithreaded local backend that runs locally. When your local machine becomes insufficient, it can be scaled to run on a distributed cluster.

Pexpect makes Python an easier tool for controlling other programs. Pexpect can be used to create child applications, control them and respond to their output. Pexpect works like Don Libes' Expect. Your script can use Pexpect to create a child application. You can also control it like a human would. Pexpect is a tool that automates interactive applications like ssh and FTP, passwd and telnet. It can also be used to automate setup scripts that duplicate software package installations on different servers. It can also be used to automate software testing. Pexpect has a similar spirit to Don Libes' Expect but is pure Python. Pexpect is not like other Expect-like Python modules. It doesn't require Expect or TCL to be compiled. It should work on any platform that can support the standard Python pty modules. The Pexpect interface is easy to use.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

Loupe Browser is an intuitive visualization software that allows you to explore and analyze 10x Genomics Chromium or Visium data. LoupeR can convert Seurat objects to Loupe Browser files. The Loupe Browser interface's navigation and interactive features are based on a dataset of lung squamous-cell carcinoma. The workspace is centered on the view panel, where single points representing barcodes of cells are shown in different projections. Each point represents a barcode. The vast majority of them correspond to a cell. The default projection, created by the Cell Ranger pipeline, is the tSNE plot. Other projections are also available. You can move the plot by dragging the mouse over cells. Zoom in and out using the mouse wheel or trackpad. Cluster labels will appear as you move the mouse over the plot. This is useful for data with a large number of precomputed groups.