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AI-native analytics for agentic data analysis
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Build interactive analytics apps in Python
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Create interactive charts in Python and JavaScript
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Announcing Plotly Cloud: a drag-and-drop way to publish, manage, and share Dash and Plotly Studio apps with your team or the world.
Secure OAuth in Dash apps. Learn how Dash Enterprise supports pass-through authentication to APIs like Databricks, BigQuery, and more.
Chris Parmer, Plotly co-founder and author of Dash, announces the launch of Plotly Studio, an AI-native desktop app for generating data apps and visualizing data.
Celia Lopez, Customer Success Engineer at Plotly, shares her toolkit and practical examples for building reliable data apps with logging and Dev Tools for debugging within Dash and the browser.
Learn about the flow of data used by AI from training to deployment, including real-time, contextual, and user data that powers modern AI applications.
The limitations of traditional BI tools make it hard to meet modern analytics needs. Learn how AI and data apps offer a more dynamic alternative.
Explore how agentic analytics uses AI agents to monitor KPIs, generate insights, and trigger actions autonomously in modern data workflows.
Understand the differences between BI and AI in analytics. See how the limitations of traditional BI tools can be overcome with AI and data apps.
Learn about the uv package manager, its definition and benefits, and how to approach issues and quirks in your Python workflows when using this tool.
Learn the basics of AI in data science workflows to speed up and generate Python code, automate data cleaning, and build production data apps.