Data Science Platforms
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Alteryx aims to be the launchpad for automation breakthroughs. Be it for personal growth, achieving transformative digital outcomes, or rapid innovation, Alteryx converges analytics, data science and process automation to enable users across organizations to make business-altering…
The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to…
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SAP Predictive Analytics is, as the name would suggest, a statistical analysis and data mining platform that can be deployed with SAP HANA.
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides…
Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.
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Available on Microsoft's Azure platform, Data Science Virtual Machines (DSVMs) are comprehensive pre-configured virtual machines for data science modelling, development and deployment.
Iguazio, headquartered in Herzliya, provides a Data Science Platform to automate machine learning pipelines. It aims to accelerate the development, deployment and management of AI applications at scale, enabling data scientists to focus on delivering better, more accurate and more…
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs. Users can run data science workflows with high-speed GPU compute and parallelize data loading, data manipulation, and machine learning for 50X faster end-to-end data science pipelines.…
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.
Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global…
Explorium, headquartered in San Mateo, provides an External Data Platform that automatically discovers thousands of relevant data signals and uses them to improve analytics and machine learning. The automated Explorium Platform enables organizations to discover and use third party…
Streamlit is an open-source Python library designed to make it easy to build custom web-apps for machine learning and data science, from the company of the same name in San Francisco. Streamlit also hosts its community's Streamlit Component offered via API to help users get started.…
Hex (Hex Technologies, headquartered in San Francisco) helps people use data by building a collaborative, shareable analytics workspace. The Hex solution helps users empower users to ask new questions and share their findings in one product without any friction.
esProc SPL is an open-source and JVM-based analyzing and computing engine for structured data and semi-structured data, and capable at solving data problems, including hard to write, slow to run and difficult to operate and maintain. esProc SPL adopts self-created SPL (Structured…
Bitfount is a distributed data science platform enabling data custodians and data scientists to collaborate on federated, privacy-preserving data analysis and AI/ML such that the world's intractable data can become safely interactable. Bitfount leverages a zero-trust messaging…
Knoldus Data science platform uses a structured data program for the entire data science life cycle, including data integration and exploration, model development, and model deployment. within a single integrated environment. It combines open source and commercial analytic technology…
The Peltarion Platform is a low-code deep learning platform designed to enable users to build commercially viable AI-powered solutions, at speed and at scale. The platform allows users to build, tweak, fine-tune and deploy deep learning models. It is end-to-end and lets users do…
DataEdge, a cloud-based Data Analytics and AI Engineering platform provides modular, scalable, and API-driven solutions to unlock data-powered insights. Configured to HITRUST standards, DataEdge is a no-code platform, which can be deployed in hours without development time.
TIMi, headdquartered in Brussels, offers their eponymous framework to push the barriers of analytics, predictive analytics and Big Data, while offering an environment to develop new ideas and make business decisions.
Toric is a smart data-workspace where the user can leverage all data in one place. Toric is used by Architecture & Planning, Civil Engineering, Construction, Real Estate operations, and developers to leverage their existing data. Using Toric, companies can combine design, project,…
Brilent is a talent intelligence platform to help recruiters automatically identify the right candidates for their organization.
According to the vendor, Analance is a robust, scalable end-to-end advanced analytics platform that combines machine learning, artificial intelligence, business intelligence, and data management capabilities in one integrated, self-serve platform. The platform is built to deliver…
DataFleets is a cloud platform for unified and privacy-preserving enterprise data analytics powered by Federated Learning, aimed at making it easy to securely bridge data silos and create new data-driven products with strong network effects.DataFleets' tech boasts support for a full…
Wolfram Data Science Platform enabling a full spectrum of data science analysis and visualization and automatically generating rich interactive reports.
Incedo Lighthouse is a cloud native AI powered Decision Automation platform to develop use case specific solutions. It optimizes the journey of ‘Data -> Insights -> Actions’ at enterprise scale in near-real time by harnessing AI applied on Big Data in a low-code environment.…
FICO Analytics Workbench (replacing FICO Model Builder) is a predictive analytics and modeling tool, from FICO.
eQube®-ADA, Augmented Data Analytics, employs Machine Learning (ML) and Advanced Statistical Techniques for analytics. It identifies patterns, clusters, anomalies, offers textual insights, predictive analysis, forecasting, time series decomposition, and text similarity analysis. It…
Data Science Platforms TrustMap
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Learn More About Data Science Platforms
What are Data Science Platforms?
Data Science Platforms build predictive models using code, Machine Learning, and vast amounts of data. These models facilitate the creation of business solutions. Data science modeling can predict events and outcomes in the real world and answer questions such as ‘how much product will be sold next year?’, ‘how to optimize supply chain delivery times?’, or ‘how to reduce customer churn?’. The accuracy of the modeling depends on how well the full scope of inputs is understood, and the completeness and accuracy of data.
Their tools can handle volumes of structured and unstructured data that are not supported by traditional databases and statistical software. Data Science Platforms centralize access to multiple data sources and a variety of data science tools, including open-source tools.
These platforms are used by data scientists who capture, clean, and visualize data and use statistical analysis, machine learning capabilities, and coding skills to produce their models. It enables them to run, track, and deploy models. Machine learning engineers help data scientists scale and optimize their models. These platforms are also used by data engineers, citizen data scientists who leverage user-friendly features, and business stakeholders to better understand their business. The platforms readily support model iterations based on stakeholder feedback.
Data Science Tools
Individual data science tools are also included in this category. Data science tools do the same work as data science platforms, helping data scientists to capture, clean, analyze, and visualize data. Individual data science tools may be more limited in scope, assisting with only a portion of this process, or supporting only one programming language, while data science platforms usually support the entire data science process and multiple programming languages.
Difference Between Business Intelligence and Data Science
Business Intelligence uses data analytics to understand how the business has been performing, driving decision-making that is based on past results. This intelligence is used to operate a business in the most effective manner. Data science creates models to predict future performance, and the information these models provide helps transform a business to align with its goals.
Data Science Platforms Features
Data Science Platforms will have many of these features.
- Integrate multiple data science tools
- Centralize data resources
- Handle very large amounts of structured and unstructured data
- Data mining
- Data access, gathering, and preparation
- Data visualization
- Multiple programming language support
- No code options
- Model development and iteration
- Model deployment
- Machine learning
- Deep learning
- Collaboration
- GUI
- Drag and drop building
- Dashboards
- Automated documentation and explainers
- Permissions for access to data and models
- Security
- Audit logs
- Reporting
- Cloud-based, on-premises, hybrid installations
Data Science Platforms Comparison
Consider the following when purchasing Data Science Platforms
Data Science Platforms vs. Data Science Tools: If a variety of complex problems need to be modeled, a data science platform offers many different tools, additional features, and programming language options to meet your needs. Open-source data science tools such as Hadoop, Python, R, and MLlib Apache Spark are sufficient for modeling common and less complex problems such as customer churn.
Open vs Closed Platforms: On some platforms, you are restricted to using their proprietary language such as MATLAB. Others like Anaconda Enterprise support multiple open source programming languages.
Collaboration: Cloud-based platforms facilitate collaboration. Platforms that have robust collaboration features include RapidMiner, DataRobot, and Domino Data Lab.
Data Security: Industries and organizations that have strict data security regulatory requirements such as healthcare and governmental organizations may require an on-premises solution to satisfy those standards. Some vendors offer cloud, on-premises, and hybrid installation options.
Model Deployment: Model deployments entail software engineering skills that are often beyond the capabilities of data scientists. Your in-house expertise will determine how quickly models can be deployed. Evaluate the platform’s deployment features to understand how they may impact deployment timeframes.
In-house Expertise Requirements: Some vendors such as Anaconda Enterprise provide data science modeling environments that will require your in-house data science team to produce all the necessary coding. Others such as Alteryx offer additional support and a more automated approach to data modeling.
Transparency: Platforms shed light on the model they produce by including automated documentation and explainer functionality which promotes data governance and helps reduce bias, compliance, and financial risk. Determine that the features of your selection meet your business’s requirements.
Pricing Information
Data Science Platforms can be expensive starting at around $1,000 a year and ranging over $50,000 a year depending on the tools, features, the number of users, and amount of data that can be supported. Installation fees costing a few thousand dollars are often added to the price. Some vendors will require obtaining a price quote.
More Resources
TrustRadius blog resources for Data Science.
Distinguishing Between Business Intelligence, Business Analytics, and Data Science
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Frequently Asked Questions
What do Data Science Platforms do?
What are the benefits of using Data Science Platforms
What are the best Data Science platforms?
These are some of the most popular Data Science platforms.