Dataiku is a single, end-to-end platform for building and managing analytics, models, and agents across your organization. It provides no-, low-, and full-code interfaces so data scientists, analysts, and business users can all build AI using their existing skills. Dataiku works with any cloud provider, data platform, and GenAI service, ensuring infrastructure freedom and avoiding vendor lock-in. Built-in governance and monitoring give you the visibility and control to confidently deploy AI at scale.
Alteryx One Platform is a software designed to support data preparation, analytics automation, and machine learning processes within organizations. The software offers tools for data blending, integration of disparate sources, and automating workflows, enabling users to transform raw data into actionable insights. Its features include code-free and code-friendly capabilities, collaboration options, and integration with various cloud and on-premises data systems. Alteryx One Platform is used to address business problems related to data-driven decision making, operational efficiency, and analytics scalability. It aims to simplify the process of building analytical models and sharing results across teams, supporting organizations in extracting value from data assets.
DataRobot is agentic AI for the workforce. Our agentic platform enables frontline/business teams to develop, deliver, and govern AI agents and applications that work intelligently and securely with core business processes, infrastructure, and systems — maximizing impact and minimizing risk for organizations across industries.
Amazon SageMaker is a software that enables developers and data scientists to build, train, and deploy machine learning models at scale. The software offers a managed environment that supports various machine learning frameworks and algorithms, including built-in tools for data labeling, model tuning, and data preparation. It provides infrastructure automation for distributed training, as well as model hosting for real-time and batch inference. Users can take advantage of integrated Jupyter notebooks to perform data exploration and preprocessing. Amazon SageMaker supports deployment across cloud and edge environments, helping organizations accelerate and standardize machine learning workflows. The software addresses the challenges of operationalizing machine learning by streamlining development and deployment processes.
MATLAB is a software developed for numerical computing, data analysis, and algorithm development. It provides an environment for matrix manipulations, plotting of functions and data, implementation of algorithms, and creation of user interfaces. The software supports integration with other languages and applications, and it is widely used for tasks such as data visualization, simulation, and prototyping in engineering, scientific research, and computational finance. MATLAB is utilized for solving complex mathematical problems, automating computations, and facilitating the modeling and simulation of systems. It addresses business challenges by enabling users to efficiently analyze data, develop algorithms, and generate reports or applications within a unified platform.
Altair RapidMiner is a software designed for data science, machine learning, and advanced analytics. It offers a visual workflow designer that enables users to prepare data, build predictive models, and operationalize analytics projects. The software supports a range of data sources and methods, allowing integration with databases and cloud storage, as well as the application of various data preparation, transformation, and modeling techniques. Altair RapidMiner provides tools for data exploration, model validation, automation, and deployment, addressing business problems such as component failure prediction, risk analytics, customer segmentation, and forecasting. It aims to help organizations derive insights from large datasets and streamline decision-making processes through accessible machine learning and analytical capabilities.
IBM SPSS Statistics is a robust statistical software that enables users of any statistical expertise to analyze data and gain actionable insights to drive quality decision-making.
SPSS Statistics provides an intuitive interface and low-code approach, enabling users to derive precise results through ad-hoc analysis, data management, advanced statistical procedures, and modeling techniques.
- Simplify complex data analysis using advanced statistical techniques that address all facets of the analytical journey, from data preparation and management, to analysis and reporting.
- Generate predictive analysis using advanced forecasting procedures such as regression models, time series modeler, and seasonal decomposition to uncover patterns and predict future trends.
- Create compelling visual representations to quickly explore your data, formulate hypotheses, identify trends, derive accurate conclusions and deliver graphs and presentation-ready reports to communicate results.
Databricks Data Intelligence Platform is a software designed to unify data, analytics, and artificial intelligence workloads under a single platform. It enables organizations to store, manage, and analyze structured and unstructured data at scale while supporting collaborative data engineering, machine learning, and business intelligence projects. The software provides tools for data warehousing, data lakehouse integration, automated data workflows, and governance capabilities, facilitating secure sharing and discovery of data assets. By streamlining the creation of analytics solutions, Databricks Data Intelligence Platform aids businesses in deriving insights, building machine learning models, and operationalizing data science processes to address complex analytical tasks and inform decision-making.
Base SAS is a software that provides a programming environment designed for data management, analysis, and reporting. It supports a range of data manipulation techniques, including data access, transformation, and cleaning. The software includes a structured query language for database operations, data step programming for advanced processing, and built-in procedures for statistical analysis and reporting. Base SAS enables users to integrate with different data sources and file formats, supporting large-scale data organization and retrieval. It addresses business needs related to handling and analyzing extensive datasets, automating data processing tasks, and producing results in a reproducible and consistent format. The software is widely utilized for converting raw data into structured information to support decision-making processes.
Anaconda AI Platform is a software designed to facilitate data science, machine learning, and artificial intelligence workflows. The software provides an integrated environment for developing, managing, and deploying AI-driven applications. It offers features such as support for popular programming languages, access to a repository of open-source packages, and collaborative workspace tools. Anaconda AI Platform enables users to manage environments, handle dependencies, and simplify the process of scaling AI models across teams or cloud infrastructures. The software addresses challenges related to reproducibility, security, and operationalization in the deployment of data-driven projects within various business contexts.
Posit Team is a software that provides collaborative data science capabilities for organizations working with the R and Python programming languages. The software enables teams to share code, manage projects, and coordinate workflows in a centralized environment. It offers integrated tools for version control, access management, and resource allocation, supporting both on-premises and cloud deployments. Posit Team helps users streamline analysis, enhance productivity, and maintain reproducibility in data-driven projects. The software addresses challenges related to sharing insights, organizing research efforts, and ensuring consistent project execution within teams working on statistical analysis, machine learning, and data visualization tasks.
SAS Enterprise Guide is a software that provides a graphical interface for advanced analytics, data management, and reporting. This software enables users to access, manipulate, and analyze data from multiple sources with point-and-click tasks, code editing, and automation capabilities. It supports data integration, statistical analysis, and the development of repeatable processes for data exploration and reporting. SAS Enterprise Guide offers features that facilitate collaboration among users through projects and workflows, enhancing productivity for business analysts and statisticians. The software addresses business needs for efficient data preparation, analysis, and sharing of results within organizations seeking to streamline decision-making based on data insights.
KNIME offers a complete platform for end-to-end data science, from creating analytic models, to deploying them & sharing insights, through to data apps & services. KNIME Analytics Platform is free & open source. Users remain on the bleeding edge of data science, get 300+ connectors to data sources & integrations to all popular ML libraries. KNIME Business Hub provides a single, customer-managed environment for data workers to collaborate on & deploy solutions. KNIME enables data experts to accelerate time to insight, collaborate with other disciplines & upskill colleagues. Business & domain experts can access & blend data, perform advanced analyses & deliver timely insights in a visual, interactive environment that eliminates the need to code. End users get instant insights with custom, interactive data apps without coding or building analytical models. MLOps & IT can securely deploy, manage & scale with a single installation & ensure enterprise-grade security & governance.
Microsoft Azure Machine Learning is a software designed to facilitate the development, deployment, and management of machine learning models. The software provides tools for data preparation, feature engineering, model training, and evaluation across various algorithms. It offers scalable cloud-based resources supporting automated machine learning processes and collaborative workflows for data scientists and developers. Built-in support for version control, reproducibility, and experimentation helps users track progress and refine models. The software integrates with other Azure services to streamline model deployment into production environments. It addresses the business problem of operationalizing machine learning and artificial intelligence, enabling organizations to accelerate predictive analytics and decision automation while maintaining governance and security standards.
Vertex AI is a software developed by Google that facilitates the building, deployment, and management of machine learning models in cloud environments. The software integrates tools for data labeling, model training, hyperparameter tuning, and model evaluation, supporting both custom and pre-trained models. It allows users to operationalize models with monitoring and automated deployment features, while providing scalability across various data types and use cases. Vertex AI addresses business challenges related to implementing machine learning solutions by offering a unified platform to streamline workflows, reduce maintenance complexities, and enable version control and collaboration among teams.
Domino Enterprise AI Platform is a software designed to enable data science, IT, and AI/ML teams to develop, deploy, and manage models within enterprise environments. The software provides a centralized platform for collaboration, reproducibility, and governance of data workflows, supporting various programming languages and tools. It integrates with existing infrastructure, facilitating access to scalable computing resources and version control for experiments. By streamlining model lifecycle management, the software addresses business challenges related to operationalizing artificial intelligence initiatives, ensuring compliance, and improving productivity across analytics-driven projects. Domino is the open platform to industrialize all AI and drive real outcomes — applications, models, and agents — by connecting your existing infrastructure to deliver value on a secure, scalable platform with built-in cost optimization and governance.
IBM watsonx is a software platform designed to facilitate the development, training, and deployment of artificial intelligence models and applications. The software provides tools for foundation model management, generative AI workflows, and data governance, allowing organizations to build custom AI solutions tailored to specific business needs. It supports data preparation, model lifecycle management, and observability, aiming to address challenges related to scalable AI implementation and compliance. By integrating capabilities for accessing structured and unstructured data, IBM watsonx seeks to streamline workflows in environments that require automation, decision support, and advanced analytics, assisting organizations in managing the complexities associated with operationalizing artificial intelligence.
H2O AI Cloud is a software platform designed to support the development, deployment, and management of artificial intelligence and machine learning models. The software offers tools for data preparation, model training, automated machine learning, and interpretability, enabling users to build and operate AI models at scale. It provides a collaborative environment for teams to work on projects and includes features such as data ingestion, model versioning, monitoring, and pipeline management. H2O AI Cloud aims to address business challenges related to data-driven decision making and operationalizing machine learning models across various industries.
SAS Enterprise Miner is a software designed for data mining and predictive modeling, enabling users to analyze large volumes of data and discover patterns or relationships. The software provides capabilities for building, testing, and comparing predictive models using machine learning algorithms, statistical techniques, and data transformation tools. It supports automated modeling, variable selection, and data exploration, helping organizations address business challenges related to forecasting, customer segmentation, risk assessment, and resource optimization. The software integrates with various data sources and offers a graphical interface that facilitates workflow design and collaboration among analysts and data scientists.
SAS Viya is a software platform designed for data management, analytics, and artificial intelligence tasks. It supports a range of analytical methods including data preparation, statistical analysis, machine learning, and deep learning. The software enables users to integrate data from multiple sources, perform complex data transformations, and build analytical models. SAS Viya supports collaborative workflows, allowing different users to access and work on data projects using a common platform. It offers programming interfaces in languages such as Python and R and provides deployment options that accommodate on-premise, cloud, and hybrid environments. The software addresses the challenge of deriving insights from large and diverse datasets to enable data-driven decision making in organizations.
