Plotly Dash Integrations

Make is a visual platform that allows anyone to design, build and automate anything, from workflows and tasks to apps and systems. It does not require any coding. Make is used by SMBs, scaleups and teams worldwide to scale their businesses faster than ever. Make allows people to connect and create workflows at lightning speed. Make allows anyone to build like a developer and launch solutions across all industries and business areas at a fraction the cost and time. Make allows teams to visualize and modify processes, and collaborate on them so they can scale as fast as their company. You can integrate sales and marketing tools, automate a customer journey, improve business operations, or build a custom backend system. Making on Make is intuitive, playful, and powerful. Our Maker community has shown us that when building brings out joy, there is no limit to what is possible.

MATLAB® offers a desktop environment specifically optimized for iterative design and analysis, paired with a programming language that allows for straightforward expression of matrix and array mathematics. It features the Live Editor, which enables users to create scripts that merge code, output, and formatted text within an interactive notebook. The toolboxes provided by MATLAB are meticulously developed, thoroughly tested, and comprehensively documented. Additionally, MATLAB applications allow users to visualize how various algorithms interact with their data. You can refine your results through repeated iterations and then easily generate a MATLAB program to replicate or automate your processes. The platform also allows for scaling analyses across clusters, GPUs, and cloud environments with minimal modifications to your existing code. There is no need to overhaul your programming practices or master complex big data techniques. You can automatically convert MATLAB algorithms into C/C++, HDL, and CUDA code, enabling execution on embedded processors or FPGA/ASIC systems. Furthermore, when used in conjunction with Simulink, MATLAB enhances the support for Model-Based Design methodologies, making it a versatile tool for engineers and researchers alike. This adaptability makes MATLAB an essential resource for tackling a wide range of computational challenges.

Tableau, an industry-leading analytics platform, empowers businesses to make smarter, data-driven decisions with AI-powered insights and advanced data visualization. By leveraging Tableau Next, which integrates seamlessly with Salesforce and Agentforce, users can access intelligent analytics and unlock the full potential of their data. Tableau provides flexible deployment options—whether cloud-based, on-premises, or directly integrated with Salesforce CRM—ensuring organizations can access a comprehensive data management solution. With built-in AI and machine learning capabilities, Tableau helps users uncover patterns, predict outcomes, and improve decision-making at every level of the organization. Its intuitive interface allows analysts, business leaders, and IT teams to explore data, visualize trends, and collaborate efficiently, while fostering a Data Culture that accelerates innovation and enhances operational efficiency.

At the heart of extensible programming lies the definition of functions. Python supports both mandatory and optional parameters, keyword arguments, and even allows for arbitrary lists of arguments. Regardless of whether you're just starting out in programming or you have years of experience, Python is accessible and straightforward to learn. This programming language is particularly welcoming for beginners, while still offering depth for those familiar with other programming environments. The subsequent sections provide an excellent foundation to embark on your Python programming journey! The vibrant community organizes numerous conferences and meetups for collaborative coding and sharing ideas. Additionally, Python's extensive documentation serves as a valuable resource, and the mailing lists keep users connected. The Python Package Index (PyPI) features a vast array of third-party modules that enrich the Python experience. With both the standard library and community-contributed modules, Python opens the door to limitless programming possibilities, making it a versatile choice for developers of all levels.

data.world is a cloud-native service meticulously designed for contemporary data architectures, ensuring seamless management of updates, migrations, and ongoing maintenance. This streamlined setup process is complemented by a vast and expanding ecosystem of pre-built integrations with all major cloud data warehouses. When prompt results are essential, your team should concentrate on addressing genuine business challenges rather than grappling with cumbersome data management software. data.world simplifies the process for all users, not just data experts, enabling them to obtain clear, precise, and prompt answers to various business inquiries. Our platform features a cloud-based data catalog that connects isolated and distributed data to well-known business concepts, fostering a cohesive knowledge base that everyone can access, comprehend, and utilize. Furthermore, beyond our enterprise solutions, data.world hosts the largest collaborative open data community globally, where individuals collaborate on diverse projects ranging from social bot detection to acclaimed data journalism initiatives, promoting innovation and shared learning. This unique environment encourages knowledge sharing and empowers users to leverage data in creative and impactful ways.

Neptune.ai serves as a robust platform for machine learning operations (MLOps), aimed at simplifying the management of experiment tracking, organization, and sharing within the model-building process. It offers a thorough environment for data scientists and machine learning engineers to log data, visualize outcomes, and compare various model training sessions, datasets, hyperparameters, and performance metrics in real-time. Seamlessly integrating with widely-used machine learning libraries, Neptune.ai allows teams to effectively oversee both their research and production processes. Its features promote collaboration, version control, and reproducibility of experiments, ultimately boosting productivity and ensuring that machine learning initiatives are transparent and thoroughly documented throughout their entire lifecycle. This platform not only enhances team efficiency but also provides a structured approach to managing complex machine learning workflows.

R is a comprehensive environment and programming language tailored for statistical analysis and graphical representation. As a part of the GNU project, it shares similarities with the S language, which was originally designed by John Chambers and his team at Bell Laboratories, now known as Lucent Technologies. Essentially, R serves as an alternative implementation of S, and while there are notable distinctions between the two, a significant amount of S code can be executed in R without modification. This versatile language offers a broad spectrum of statistical methods, including both linear and nonlinear modeling, classical statistical tests, time-series analytics, classification, and clustering, among others, and it boasts a high level of extensibility. The S language is frequently utilized in research focused on statistical methodologies, and R presents an Open Source avenue for engaging in this field. Moreover, one of R's key advantages lies in its capability to generate high-quality publication-ready graphics, facilitating the inclusion of mathematical symbols and formulas as needed, which enhances its usability for researchers and analysts alike. Ultimately, R continues to be a powerful tool for those seeking to explore and visualize data effectively.

From its inception, Julia was crafted for optimal performance. Programs written in Julia compile into efficient native code across various platforms through the LLVM framework. Utilizing multiple dispatch as its foundational paradigm, Julia simplifies the representation of numerous object-oriented and functional programming concepts. The discussion on the Remarkable Effectiveness of Multiple Dispatch sheds light on its exceptional performance. Julia features dynamic typing, giving it a scripting language feel, while also supporting interactive sessions effectively. Furthermore, Julia includes capabilities for asynchronous I/O, metaprogramming, debugging, logging, profiling, and a package manager, among other features. Developers can create entire applications and microservices using Julia's robust ecosystem. This open-source project boasts contributions from over 1,000 developers and is licensed under the MIT License, emphasizing its community-driven nature. Overall, Julia’s combination of performance and flexibility makes it a powerful tool for modern programming needs.

F# offers a blend of simplicity and conciseness akin to Python, while also delivering correctness, robustness, and performance that surpasses that of C# or Java. It is an open-source and cross-platform language that comes at no cost, equipped with professional-grade tools. F# serves as a powerful language for web development, cloud computing, data science, applications, and more, seamlessly integrating with both JavaScript and .NET. In the realm of cloud computing, the ability to utilize multiple interconnected services is essential. This necessitates a distinctive combination of technologies and capabilities where F# truly shines. The growing popularity of cloud solutions has made it increasingly straightforward to deploy various services in the cloud, broadening the scope of possibilities by facilitating the storage of vast data sets and executing complex computations across distributed machine clusters. As more developers adopt F#, the potential for innovative cloud-based applications continues to expand dramatically.

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.

Kedro serves as a robust framework for establishing clean data science practices. By integrating principles from software engineering, it enhances the efficiency of machine-learning initiatives. Within a Kedro project, you will find a structured approach to managing intricate data workflows and machine-learning pipelines. This allows you to minimize the time spent on cumbersome implementation tasks and concentrate on addressing innovative challenges. Kedro also standardizes the creation of data science code, fostering effective collaboration among team members in problem-solving endeavors. Transitioning smoothly from development to production becomes effortless with exploratory code that can evolve into reproducible, maintainable, and modular experiments. Additionally, Kedro features a set of lightweight data connectors designed to facilitate the saving and loading of data across various file formats and storage systems, making data management more versatile and user-friendly. Ultimately, this framework empowers data scientists to work more effectively and with greater confidence in their projects.

Enhance your professional presence and career trajectory through exceptional analytics and presentations. Eliminate the monotonous coding and design tasks that often slow you down. By utilizing skills.ai, you can effectively leverage AI technology to quickly generate comprehensive analytics, paving the way for seamless success for you and your team. This innovative platform simplifies the data analysis process, allowing users to prioritize insights and make informed decisions without the burden of intricate coding or data handling. Additionally, skills.ai's data chat feature transforms data analytics into a user-friendly experience, enabling you to engage with your data effortlessly, asking questions in a conversational manner just like you would with a trusted data analyst. Discover how skills.ai can empower you to unlock your full potential in data-driven environments.

OpenMemory is a Chrome extension that introduces a universal memory layer for AI tools accessed through browsers, enabling the capture of context from your engagements with platforms like ChatGPT, Claude, and Perplexity, ensuring that every AI resumes from the last point of interaction. It automatically retrieves your preferences, project setups, progress notes, and tailored instructions across various sessions and platforms, enhancing prompts with contextually rich snippets for more personalized and relevant replies. With a single click, you can sync from ChatGPT to retain existing memories and make them accessible across all devices, while detailed controls allow you to view, modify, or disable memories for particular tools or sessions as needed. This extension is crafted to be lightweight and secure, promoting effortless synchronization across devices, and it integrates smoothly with major AI chat interfaces through an intuitive toolbar. Additionally, it provides workflow templates that cater to diverse use cases, such as conducting code reviews, taking research notes, and facilitating creative brainstorming sessions, ultimately streamlining your interaction with AI tools.

Spotfire stands out as the most comprehensive analytics solution available, empowering users to delve into and visualize new insights from data through engaging dashboards and sophisticated analytics tools. The platform offers extensive capabilities, such as predictive analytics, geolocation analytics, and streaming analytics, all designed to operate at scale. Additionally, with the introduction of Spotfire Mods, users can quickly and repeatedly create customized analytic applications that can grow alongside their needs. The seamless experience provided by the Spotfire analytics platform merges visual analytics, data exploration, and intuitive insights all in one interface. Users can engage deeply with both historical and real-time data in an interactive manner, effortlessly navigating through various multi-layered, disparate data sources using fully brush-linked and responsive visualizations. Envision the possibilities and swiftly develop scalable, customized analytics applications with the Spotfire Mods framework, ensuring you harness the full capabilities of Spotfire software in your tailored analytics solutions. As the demand for data-driven decision-making grows, Spotfire remains at the forefront of delivering innovative and efficient analytics tools for diverse users.

If you're looking to establish a governed delta lake, create a data warehouse, or transition from a conventional database to a contemporary cloud data solution, Lyftrondata has you covered. You can effortlessly create and oversee all your data workloads within a single platform, automating the construction of your pipeline and warehouse. Instantly analyze your data using ANSI SQL and business intelligence or machine learning tools, and easily share your findings without the need for custom coding. This functionality enhances the efficiency of your data teams and accelerates the realization of value. You can define, categorize, and locate all data sets in one centralized location, enabling seamless sharing with peers without the complexity of coding, thus fostering insightful data-driven decisions. This capability is particularly advantageous for organizations wishing to store their data once, share it with various experts, and leverage it repeatedly for both current and future needs. In addition, you can define datasets, execute SQL transformations, or migrate your existing SQL data processing workflows to any cloud data warehouse of your choice, ensuring flexibility and scalability in your data management strategy.

ThinkData Works provides a robust catalog platform for discovering, managing, and sharing data from both internal and external sources. Enrichment solutions combine partner data with your existing datasets to produce uniquely valuable assets that can be shared across your entire organization. The ThinkData Works platform and enrichment solutions make data teams more efficient, improve project outcomes, replace multiple existing tech solutions, and provide you with a competitive advantage.

Voxel51 is the driving force behind FiftyOne, an open-source toolkit designed to enhance computer vision workflows by elevating dataset quality and providing valuable insights into model performance. With FiftyOne, you can explore, search through, and segment your datasets to quickly locate samples and labels that fit your specific needs. The toolkit offers seamless integration with popular public datasets such as COCO, Open Images, and ActivityNet, while also allowing you to create custom datasets from the ground up. Recognizing that data quality is a crucial factor affecting model performance, FiftyOne empowers users to pinpoint, visualize, and remedy the failure modes of their models. Manual identification of annotation errors can be labor-intensive and inefficient, but FiftyOne streamlines this process by automatically detecting and correcting label inaccuracies, enabling the curation of datasets with superior quality. In addition, traditional performance metrics and manual debugging methods are often insufficient for scaling, which is where the FiftyOne Brain comes into play, facilitating the identification of edge cases, the mining of new training samples, and offering a host of other advanced features to enhance your workflow. Overall, FiftyOne significantly optimizes the way you manage and improve your computer vision projects.

The RAPIDS software library suite, designed on CUDA-X AI, empowers users to run comprehensive data science and analytics workflows entirely on GPUs. It utilizes NVIDIA® CUDA® primitives for optimizing low-level computations while providing user-friendly Python interfaces that leverage GPU parallelism and high-speed memory access. Additionally, RAPIDS emphasizes essential data preparation processes tailored for analytics and data science, featuring a familiar DataFrame API that seamlessly integrates with various machine learning algorithms to enhance pipeline efficiency without incurring the usual serialization overhead. Moreover, it supports multi-node and multi-GPU setups, enabling significantly faster processing and training on considerably larger datasets. By incorporating RAPIDS, you can enhance your Python data science workflows with minimal code modifications and without the need to learn any new tools. This approach not only streamlines the model iteration process but also facilitates more frequent deployments, ultimately leading to improved machine learning model accuracy. As a result, RAPIDS significantly transforms the landscape of data science, making it more efficient and accessible.

Enhance your organization's machine learning capabilities through seamless management, deployment, and monitoring on a premier AI platform, all powered by navio. This tool enables the execution of a wide range of machine learning operations throughout your entire AI ecosystem. Transition your experiments from the lab to real-world applications, seamlessly incorporating machine learning into your operations for tangible business results. Navio supports you at every stage of the model development journey, from initial creation to deployment in a production environment. With automatic REST endpoint generation, you can easily monitor interactions with your model across different users and systems. Concentrate on exploring and fine-tuning your models to achieve optimal outcomes, while navio streamlines the setup of infrastructure and auxiliary features, saving you valuable time and resources. By allowing navio to manage the entire process of operationalizing your models, you can rapidly bring your machine learning innovations to market and start realizing their potential impact. This approach not only enhances efficiency but also boosts your organization's overall productivity in leveraging AI technologies.

Coordinate, explore, and oversee all projects within your data science team efficiently. With Zepl's advanced search functionality, you can easily find and repurpose both models and code. The enterprise collaboration platform provided by Zepl allows you to query data from various sources like Snowflake, Athena, or Redshift while developing your models using Python. Enhance your data interaction with pivoting and dynamic forms that feature visualization tools such as heatmaps, radar, and Sankey charts. Each time you execute your notebook, Zepl generates a new container, ensuring a consistent environment for your model runs. Collaborate with teammates in a shared workspace in real time, or leave feedback on notebooks for asynchronous communication. Utilize precise access controls to manage how your work is shared, granting others read, edit, and execute permissions to facilitate teamwork and distribution. All notebooks benefit from automatic saving and version control, allowing you to easily name, oversee, and revert to previous versions through a user-friendly interface, along with smooth exporting capabilities to Github. Additionally, the platform supports integration with external tools, further streamlining your workflow and enhancing productivity.