AI for the enterprise
Run AI reliably and responsibly: deployment, monitoring, risk controls and compliance.
MLOps (and LLMOps for generative AI) are the engineering practices that take models to production and keep them working: versioning, automated tests, deployment, monitoring and rollback.
AI governance decides which uses are allowed, how risk is assessed, how data is protected and how decisions are explained. Regulation such as the EU AI Act and standards such as ISO/IEC 42001 have made this a board-level topic.
A register of every AI system, its owner and risk level.
Tests for accuracy, bias, safety and security before release.
Quality, drift, cost and incidents in production.
Acceptable-use rules and staff awareness.
Typical ranges, not quotes. Scope, data and team size move them most.
Models that read and write language, code and images, available as APIs or inside tools you already use.
Models that learn from your history to predict demand, risk, churn and more.
The lakehouse platform from the creators of Apache Spark: data engineering, analytics and AI in one place.
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