The Future of Governed, AI-powered Data Science

Explore how organizations deploy Posit on AWS to run governed, scalable data science workflows, unify R and Python development, and bring AI-powered analytics from experimentation to production.

This analyst report outlines how organizations can move data science from experimentation to production with the governance, security, and collaboration controls enterprises require.

Learn how teams can combine natural language interfaces with cloud infrastructure to simplify complex data science workflows and accelerate insight generation.

NASA uses Posit’s platform to deploy interactive data science applications that support workforce planning and strategic decision-making.

Learn how TruDiagnostic uses Posit tools to accelerate research into biological aging and deliver insights from complex genomic data.

AI adoption in healthcare and life sciences is accelerating at a rapid pace, driving advancements in clinical research, diagnostics, and operations. As these technologies continue to mature, the scientific community is seeing meaningful gains in both efficiency and innovation.

Large language models, coding assistants, and natural language interfaces are making it easier to move from questions to insights while reducing the effort required to explore complex datasets and build models. Increasingly, these tools are embedded directly into existing data science environments, allowing teams to use generative AI within their workflows.

Learn why data science remains essential to building reliable AI systems, helping teams evaluate model performance, improve governance, and move from experimentation to production with confidence.

Compare leading AI governance tools and learn how they help organizations manage risk, improve compliance, and build more transparent, trustworthy AI systems.

Explore the enterprise AI trends shaping 2026, from scaling pilots into production to strengthening governance, data readiness, workforce skills, and business value.

This whitepaper introduces Positron, Posit’s next-generation code editor designed to help R and Python teams build, test, and deploy modern AI and data science applications.

This playbook examines how organizations like Roche, GSK, Johnson & Johnson, and Novo Nordisk adopted open-source data science to accelerate clinical reporting, improve collaboration, and meet strict regulatory requirements.

This feature explores how insurers and data scientists are evolving their workflows and governance models to build scalable, reproducible analytics systems that meet regulatory expectations.

This whitepaper explores how combining Posit with modern data platforms enables organizations to scale analytics workflows and deploy data products faster.

This webinar explores how insurers are applying advanced analytics and AI to improve underwriting, risk modeling, and customer insights.

Learn how life sciences teams use interactive applications to accelerate clinical research insights and improve collaboration across study teams.

This webinar explores how organizations can combine human expertise with AI automation to produce trustworthy analytics and insights.

Learn how developers are building agent-driven AI applications using modern development tools and cloud infrastructure.

Hear how Johnson & Johnson adopted open-source data science to support collaboration, transparency, and reproducibility across global research teams.

As AI applications become more complex, evaluating their real-world performance is critical. Learn how data science helps teams measure, test, and improve AI systems at scale.