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What the EU AI Act means for AI governance
Teams shipping AI in the EU must be able to demonstrate compliance. Here's how LaunchDarkly helps.
Marek Poliks
MLOps Solutions for Production Machine Learning
Learn how MLOps solutions support experiment tracking, model serving, monitoring, feature management, governance, and production rollouts.
Machine learning model deployment
Machine learning model deployment moves trained models to production. Learn deployment patterns for ML models, CI/CD, and feature-flag rollouts.
Scarlett Attensil
MLOps Solutions for Production Machine Learning
Learn how MLOps solutions support experiment tracking, model serving, monitoring, feature management, governance, and production rollouts.
Scarlett Attensil
MLOps pipeline: Stages, tools, and deployment workflow
Learn how an MLOps pipeline manages data validation, feature engineering, training, evaluation, deployment, and production feedback loops.
Scarlett Attensil
Introducing the LaunchDarkly AI SDK
The LaunchDarkly AI SDK is available for Python and JavaScript and is the path we recommend for every new AgentControl integration.
Kelvin Yap
You can't control what you can't see
What LaunchDarkly showed live on the Control Panel: how to see what's happening in production, act on it in real time, and test on data you already trust.
Kellye King
Best CI/CD Pipelines for Containerized AI Development
Containerized AI applications require sophisticated deployment infrastructure to manage Docker images.
Scarlett Attensil
Stories from the Factory Floor: Our AI software factory saved me from an incident and I lived to tell the tale
Last summer, I shipped what I thought was a routine cleanup to production. It turned out to be a bug. But before the vast majority of users ever saw it, our AI software factory caught it and rolled back my change automatically.
Alex Engelberg
Podcast recap: Observability won’t save your agents
On a recent episode of the MonkCast, Marek Poliks spoke with James Governor about why governing agents from the outside leaves teams perpetually one step behind.
LaunchDarkly
Agent Optimization: Define what better means, and let AgentControl find it
Agent Optimization, now in beta in AgentControl, automatically searches for a better agent configuration against criteria you define.
Kelvin Yap
Why AI deployment breaks standard CI/CD
Learn why AI deployment can break standard CI/CD and how runtime controls, shadow testing, rollouts, and rollback reduce risk.
Scarlett Attensil
Entering the AI software factory era
What automating the SDLC at LaunchDarkly taught me about speed, control, and the job of an engineer.
Jonathan Nolen
Observability is not enough
With runtime control, teams can extend observability by moving beyond reactive monitoring and toward proactive remediation.
Betsy Sallee
Speed isn't the risk. Lack of control is.
Why controlling code and agents in the AI era matters—and why we built AgentControl.
Kellye King
MLOps lifecycle: Stages, workflow, and best practices
Understand the MLOps lifecycle from data preparation to monitoring.
Scarlett Attensil
AI pipeline: Preventing drift in production systems
Learn why uncontrolled AI pipeline changes can cause failures in prod.
Scarlett Attensil
Adaptive Triggers: AI that corrects itself in production
Adaptive Triggers is now available in closed beta.
Kelvin Yap
Agent Optimization: Discover better agent configurations automatically
Agent Optimization is now available in private beta for eligible customers.
Kelvin Yap
The next era of software needs runtime control
Edith Harbaugh
Introducing AgentControl
AgentControl is the operational layer for managing agents in production.
Kelvin Yap
LLM observability: Tutorial and best practices
LLM observability analyzes how models behave across development, testing, and production.
Scarlett Attensil
LLM pricing comparison: Tutorial and best practices
Large language models (LLMs) power a wide range of AI applications today.
Scarlett Attensil
Agent graphs bring control and visibility to multi-agent AI workflows
Agent graphs bring real-time control to multi-agent AI workflows.
Kelvin Yap
