Data Science and Machine Learning Platforms Resources
Articles, Glossary Terms, Discussions, and Reports to expand your knowledge on Data Science and Machine Learning Platforms
Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, feature definitions, discussions from users like you, and reports from industry data.
Data Science and Machine Learning Platforms Articles
Seq2Seq Models: How They Work and Why They Matter in AI
10 Best Data Labeling Software With G2 User Reviews
What Is Artificial Intelligence (AI)? Types, Definition And Examples
What Is Artificial General Intelligence (AGI)? The Future Is Here
2023 Trends in AI: Cheaper, Easier-to-Use AI to the Rescue
Barriers Toward Adopting AI and Analytics in the Supply Chain
The Importance of Data Quality and Commoditization of Algorithms
How to Choose a Data Science and Machine Learning Platform That’s Right For Your Business
Data Trends in 2022
How to Make Algorithms Which Explain Themselves
Artificial Intelligence in Healthcare: Benefits, Myths, and Limitations
The Role of Artificial Intelligence in Accounting
Tech Companies Bridging the Gap Between AI and Automation
How COVID-19 Is Impacting Data Professionals
True Data Protection Demands More Than Just Regulation
What Is the Future of Machine Learning? We Asked 5 Experts
Data Science and Machine Learning Platforms Glossary Terms
Data Science and Machine Learning Platforms Discussions
Alteryx One supports global collaboration through Alteryx Server, Alteryx Gallery, and Alteryx Connect. These components centralize workflows, permissions, and shared data assets in a governed environment.
Distributed teams can run approved workflows, access trusted analytics, and reuse documented assets across time zones with consistency and control.
Depending on deployment, organizations can also use localization and multi-language features to support regional teams while maintaining governance and security.
What types of workflows benefit most from being centrally managed versus built locally by regional teams?
Alteryx One provides governed, enterprise-grade options for sharing insights with stakeholders who don’t have a full platform license. Instead of relying on generic exports, teams can distribute insights through Alteryx’s built-in sharing and execution capabilities designed for broad, secure access.
Key ways organizations share insights include:
- Viewer Access: Non-licensed stakeholders can access insights through free Viewer seats included in Alteryx One Enterprise, allowing them to open, interact with, and consume analytic outputs without building workflows themselves.
- Analytic Apps: Teams can publish pre-built analytic apps that business users can run with their own inputs—without needing a Designer or Full license.
- Governed Workflow Execution: Using Server or Plans, teams can schedule or trigger workflows so stakeholders automatically receive refreshed outputs as part of a controlled, auditable process.
- Cloud Reporting & Auto Insights: Insights can be delivered through cloud-hosted, narrative-rich reports or Auto Insights dashboards, enabling stakeholders to consume AI-generated explanations and visualizations without accessing Designer.
This approach lets executives, managers, and frontline teams interact with insights through secure, governed channels—without requiring an Alteryx license—while ensuring IT maintains full control over data access, audit trails, and sharing workflows.
How do teams decide which insights need to stay interactive versus shared as static outputs?
Alteryx delivers enterprise-grade scalability by combining advanced automation, self-service analytics, and broad data integration in a single platform. Unlike traditional BI tools that rely heavily on IT or manual data prep, Alteryx allows analysts and business users to build repeatable workflows, automate data pipelines, and publish insights without coding.
For IT and data teams, Alteryx provides strong governance, API-based integrations (Snowflake, Databricks, Tableau, Power BI), and the ability to operationalize analytics at scale. This reduces bottlenecks and frees up engineering resources, while giving executives faster time-to-insight and clearer ROI.
At what point does Alteryx usually become a better fit than relying only on dashboards and reports?



















