NVIDIA Merlin Alternatives

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.

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

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

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

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

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

Build and solve complex optimization models to identify the best possible actions. IBM® ILOG® CPLEX® Optimization Studio uses decision optimization technology to optimize your business decisions, develop and deploy optimization models quickly, and create real-world applications that can significantly improve business outcomes. How? IBM ILOG CPLEX Optimization Studio is a prescriptive analytics solution that enables rapid development and deployment of decision optimization models using mathematical and constraint programming. It combines a fully featured integrated development environment that supports Optimization Programming Language (OPL) and the high-performance CPLEX and CP Optimizer solvers. It’s data science for your decisions. IBM Decision Optimization is also available within Cloud Pak for Data where you can combine optimization and machine learning within a unified environment, IBM Watson® Studio, that enables AI-infused optimization modeling capabilities.

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

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

Democratize, collaborate, and operationalize, machine learning across your organization. Data science is a team sport. Data scientists, citizen data scientists, data engineers, business users, and developers need flexible and extensible tools that promote collaboration, automation, and reuse of analytic workflows. But algorithms are only one piece of the advanced analytic puzzle. To deliver predictive insights, companies need to increase focus on the deployment, management, and monitoring of analytic models. Smart businesses rely on platforms that support the end-to-end analytics lifecycle while providing enterprise security and governance. TIBCO® Data Science software helps organizations innovate and solve complex problems faster to ensure predictive findings quickly turn into optimal outcomes. TIBCO Data Science allows organizations to expand data science deployments across the organization by providing flexible authoring and deployment capabilities.

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

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

Design and develop data-intensive projects with human-friendly, open-source Metaflow. Run, scale, and deploy them reliably on the fully managed Outerbounds platform. One platform for all your ML and data science projects. Access data securely from your existing data warehouses. Compute with a cluster optimized for scale and cost. 24/7 managed orchestration for production workflows. Use results to power any application. Give your data scientists superpowers, approved by your engineers. Outerbounds Platform allows data scientists to develop rapidly, experiment at scale, and deploy to production confidently. All within the outer bounds of policies and processes defined by your engineers, running on your cloud account, fully managed by us. Security is in our DNA, not at the perimeter. The platform adapts to your policies and compliance requirements through multiple layers of security. Centralized auth, a strict permission boundary, and granular task execution roles.

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

Brilent is a data science tech company developing a SaaS solution to help talent seekers quickly and effectively identify the right talent to hire. The exciting part about this intelligent technology is its simplicity. No tricks. It leverages the components recruiters find quite relevant. At the core of the Brilent engine are three simple elements: the job requirements; the candidate profile; and our unique repository of market data. Next comes the fun part. Our system gathers all the relevant data from the job requirements and candidate profiles. Using hundreds of variables from these familiar recruiting elements and the market data, we use our well-honed experience to apply artificial intelligence and machine learning algorithms to predict how likely a candidate is a good fit for a particular job. In other words, a whole lot of data crunching, and completed in seconds. Recruiters get a ranking of candidates according to the desired specifications.

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

Appsilon provides innovative data analytics, machine learning, and managed services solutions for Fortune 500 companies, NGOs, and non-profit organizations. We deliver the world’s most advanced R Shiny applications, with a unique ability to rapidly develop and scale enterprise Shiny dashboards. Our proprietary machine learning frameworks allow us to deliver Computer Vision, NLP, and fraud detection prototypes in as little as one week. Above all, we are committed to making a positive impact on the world. Through our AI For Good Initiative, we routinely contribute our skills to projects that support the preservation of human life and the conservation of animal populations all over the globe. Recently, our team has worked to mitigate poaching in Africa with computer vision, provide satellite image analysis for assessing damage after natural disasters, and build tools to help with COVID-19 risk assessment. Appsilon is also a pioneer in open source.

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

We develop, manage and run predictive software services based on data science and machine learning. Software service for large e-commerce businesses and retail chains which uses machine learning to forecast sales and optimize stock distribution among retail stores and warehouses. Personalized product recommender for e-commerce web sites which uses real-time Bayes nets to display relevant recommended products for e-shop visitors. Software service which automatically suggests product price movements to improve profit by using statistical price and demand elasticity models. API for computing optimal path routes for batch picking time optimization in a retailer's warehouse, which is built on shortest path graph algorithms.

Accelerate machine learning from research to production with a consistent experience built for your traditional platform. With Python, R, and Scala directly in the web browser, Cloudera Data Science Workbench (CDSW) delivers a self-service experience data scientists will love. Download and experiment with the latest libraries and frameworks in customizable project environments that work just like your laptop. Cloudera Data Science Workbench provides connectivity not only to CDH and HDP but also to the systems your data science teams rely on for analysis. Cloudera Data Science Workbench lets data scientists manage their own analytics pipelines, including built-in scheduling, monitoring, and email alerting. Quickly develop and prototype new machine learning projects and easily deploy them to production.

Combining Data Science, Business Intelligence, and Data Management Capabilities in One Integrated, Self-Serve Platform. Analance is a robust, salable end-to-end platform that combines Data Science, Advanced Analytics, Business Intelligence, and Data Management into one integrated self-serve platform. It is built to deliver core analytical processing power to ensure data insights are accessible to everyone, performance remains consistent as the system grows, and business objectives are continuously met within a single platform. Analance is focused on turning quality data into accurate predictions allowing both data scientists and citizen data scientists with point and click pre-built algorithms and an environment for custom coding. Company – Overview Ducen IT helps Business and IT users of Fortune 1000 companies with advanced analytics, business intelligence and data management through its unique end-to-end data science platform called Analance.

dotData frees your business to focus on the results of your AI and machine learning applications, not the headaches of the data science process by automating the full data science life-cycle. Deploy full-cycle AI & ML pipeline in minutes, update in real-time with continuous deployment. Accelerate data science projects from months to days with feature engineering automation. Discover the unknown unknowns of your business automatically with data science automation. The process of using data science to develop and deploy accurate machine learning and AI models is cumbersome, time-consuming, labor-intensive, and interdisciplinary. Automate the most time-consuming and repetitive tasks that are the bane of data science work and shorten AI development times from months to days.

ZinkML is a zero-code data science platform designed to address the challenges faced by organizations in leveraging data effectively. By providing a visual and intuitive interface, it eliminates the need for extensive coding expertise, making data science accessible to a broader range of users. ZinkML streamlines the entire data science lifecycle, from data ingestion and preparation to model building, deployment, and monitoring. Users can drag-and-drop components to create complex data pipelines, explore data visually, and build predictive models without writing a single line of code. The platform also offers automated feature engineering, model selection, and hyperparameter tuning, accelerating the model development process. Moreover, ZinkML provides robust collaboration features, enabling teams to work together seamlessly on data science projects. By democratizing data science, we empower companies to extract maximum value from their data and drive better decision-making.

Kedro is the foundation for clean data science code. It borrows concepts from software engineering and applies them to machine-learning projects. A Kedro project provides scaffolding for complex data and machine-learning pipelines. You spend less time on tedious "plumbing" and focus instead on solving new problems. Kedro standardizes how data science code is created and ensures teams collaborate to solve problems easily. Make a seamless transition from development to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments. A series of lightweight data connectors is used to save and load data across many different file formats and file systems.

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

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

Spend less time preparing your data and more time analyzing it. Not only can you build better looking and more accurate reports, you can centralize all your data gathering, analytics and data science in a single interface, accessible to everyone in the organization. Import all your data no matter where it lives. Whether you’re looking to build scheduled reports from your Excel files, cross-reference data between files and databases or turn your data into SQL queryable databases, Rational BI gives you all the tools you need. Discover the signals hidden in your data, make it available without delay and move ahead of your competition. Magnify the analytics capabilities of your organization through business intelligence that makes it easy to find the latest up-to-date data and analyze it through an interface that delights both data scientists and casual data consumers.

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

Data is crucial when it comes to building, streamlining and growing your business. However, getting the full value out of data can be a challenge, many organizations are faced with poor access to data, incompatible tools, spiraling costs and slow results. Simply put, leaders who can turn raw data into real results will thrive in today’s landscape. The key to this is empowering everyone across your business to be able to analyze, build and collaborate on end-to-end AI and machine learning solutions in one place, fast. Streamflux is a one-stop shop to meet your data analytics and AI challenges. Our self-serve platform allows you the freedom to build end-to-end data solutions, uses models to answer complex questions and assesses user behaviors. Whether you’re predicting customer churn and future revenue, or generating recommendations, you can go from raw data to genuine business impact in days, not months.

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connection

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

The only all-in-one data pipeline platform. Built ground-up for a cloud-first world, Gathr is the only platform to handle all your data integration and engineering needs - ingestion, ETL, ELT, CDC, streaming analytics, data preparation, machine learning, advanced analytics and more. With Gathr, anyone can build and deploy pipelines in minutes, irrespective of skill levels. Create Ingestion pipelines in minutes, not weeks. Ingest data from any source, deliver to any destination. Build applications quickly with a wizard-based approach. Replicate data in real-time using a templatized CDC app. Native integration for all sources and targets. Best-in-class capabilities with everything you need to succeed today and tomorrow. Choose between free, pay-per-use or customize as per your requirements.

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

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

Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.

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

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

Darwin is an automated machine learning product that enables your data science and business analytics teams to move more quickly from data to meaningful results. Darwin helps organizations scale the adoption of data science across teams, and the implementation of machine learning applications across operations, becoming data-driven enterprises.

A data science platform for life scientists. Translate your microbiome research into practical applications. Bring novel, safe and effective products to market faster. Integrate the right set of data science and collaboration tools, and achieve rapid advances in cancer therapeutics. Accelerate research, enable innovation and create value by adopting effective digital technology solutions to improve crop productivity. Import your small and big data. Organize according to our templates, or define your own schema. Our format inference algorithm synthesizes parsing functions and lets you override if needed, without coding. Use our interactive import screens or CLI for bulk imports. Your data is not just bytes. Solvuu automatically computes relevant summary statistics and generates rich interactive visualizations. Explore and gain insights into your data immediately; slice and dice as needed.

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

The Domino platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record allows teams to easily find, reuse, reproduce, and build on any data science work to amplify innovation.

TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.

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

Ours is the world’s first & only end-to-end platform for all your AI-powered innovation needs — right from data cleansing & structuring, to creating & deploying advanced data-science models, to infusing advanced analytics algorithms with built-in Recommendation AI, to deducing the outcomes with easy-to-deduce visualization dashboards, as well as Explainable AI to backtrack how the outcomes were derived, our no-code AI platform can do it all! Our platform offers a holistic, seamless data science experience. With key features like a robust Data Lakehouse, a unique AI Studio, a comprehensive AI Marketplace, and a world-class data-science support team (on a need basis), FutureAnalytica is geared to reduce your time, efforts & costs across your data-science & AI journey. Initiate discussions with the leadership, followed by a quick technology assessment in 1–3 days. Build ready-to-integrate AI solutions using FA's fully automated data science & AI platform in 10–18 days.

The biggest challenge to scaling AI-powered decision-making is unused data. IBM Cloud Pak® for Data is a unified platform that delivers a data fabric to connect and access siloed data on-premises or across multiple clouds without moving it. Simplify access to data by automatically discovering and curating it to deliver actionable knowledge assets to your users, while automating policy enforcement to safeguard use. Further accelerate insights with an integrated modern cloud data warehouse. Universally safeguard data usage with privacy and usage policy enforcement across all data. Use a modern, high-performance cloud data warehouse to achieve faster insights. Empower data scientists, developers and analysts with an integrated experience to build, deploy and manage trustworthy AI models on any cloud. Supercharge analytics with Netezza, a high-performance data warehouse.

Microsoft continues its commitment and development in R, not only in the latest Machine Learning Server release, but also in the newest Microsoft R Client and Microsoft R Open releases. You can also find R and Python support in SQL Server Machine Learning Services on Windows and Linux, and R support in Azure SQL Database. R components are backwards compatible. You should be able to run existing R script on newer versions, with the exception of dependencies on packages or platforms that are no longer supported, or known issues that require a workaround or code change. Microsoft R Open is the enhanced distribution of R from Microsoft Corporation. The current release, Microsoft R Open 4.0.2, is based the statistical language R-4.0.2 and includes additional capabilities for improved performance, reproducibility and platform support. Compatibility with all packages, scripts and applications that work with R-4.0.2.

Create, embed and govern analytically driven decision flows at scale in real-time or batch. SAS Data Science Programming enables data scientists who prefer a programmatic-only approach to access SAS analytical capabilities at all stages of the analytics life cycle, including data, discovery and deployment. Visualize and discover relevant relationships in your data. Create and share interactive reports and dashboards, and use self-service analytics to quickly assess probable outcomes for smarter, more data-driven decisions. Explore data and build or adjust predictive analytical models with this solution running in SAS® Viya®. Data scientists, statisticians, and analysts can collaborate and iteratively refine models for each segment or group to make decisions based on accurate insights. Solve complex analytical problems with a comprehensive visual interface that handles all tasks in the analytics life cycle.

Merging the best of a notebook and an IDE into one integrated coding environment, experts can explore their data and write stable code at the same time with fully automated cloud infrastructure. Zerve’s data science development environment gives data science and ML teams a unified space to explore, collaborate, build, and deploy data science & AI projects like never before. Zerve offers true language interoperability, meaning that as well as being able to use Python, R, SQL, or Markdown all in the same canvas, users can connect these code blocks to each other. No more long-running code blocks or containers, with Zerve enjoying unlimited parallelization at any stage of the development journey. Analysis artifacts are automatically serialized, versioned, stored, and preserved for later use, meaning easily changing a step in the data flow without needing to rerun any preceding steps. Fine-grained selection of compute resources and extra memory for complex data transformation.

RapidMiner is reinventing enterprise AI so that anyone has the power to positively shape the future. We’re doing this by enabling ‘data loving’ people of all skill levels, across the enterprise, to rapidly create and operate AI solutions to drive immediate business impact. We offer an end-to-end platform that unifies data prep, machine learning, and model operations with a user experience that provides depth for data scientists and simplifies complex tasks for everyone else. Our Center of Excellence methodology and the RapidMiner Academy ensures customers are successful, no matter their experience or resource levels. Simplify operations, no matter how complex models are, or how they were created. Deploy, evaluate, compare, monitor, manage and swap any model. Solve your business issues faster with sharper insights and predictive models, no one understands the business problem like you do.

DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.