MLOps
Definition
MLOps is the operational discipline of deploying, monitoring, retraining, and governing machine learning models in production. It adapts DevOps practices (CI/CD, observability, versioning) to the specific failure modes of ML systems (drift, data quality, reproducibility).
Why it matters
The business case for MLOps.
Without MLOps, models in production silently degrade. Teams discover the problem when a customer or regulator surfaces it. MLOps is how organizations catch drift before it becomes damage.
Related terms in AI Workflow Automation
AI Workflow Automation
AI Workflow Automation applies artificial intelligence to decision-heavy business workflows, going beyond rule-based task automation to handle judgment, exception handling, and adaptive routing. It replaces manual decision-making at scale.
RPA (Robotic Process Automation)
Robotic Process Automation (RPA) is the use of software bots to execute repetitive, rule-based digital tasks, like data entry or system reconciliation. RPA preceded the AI workflow automation era and remains useful for narrow, predictable tasks.
Intelligent Process Automation (IPA)
Intelligent Process Automation (IPA) is the combination of RPA with AI capabilities, adding decision-making, document understanding, and adaptive routing to traditional process automation. IPA is a transitional term as the industry moves toward fully AI-native workflow automation.
Autonomous Agent
An Autonomous Agent is an AI system that executes workflows without requiring human intervention for each step, operating on goals rather than commands, using tools, and self-correcting based on outcomes.
Decision Automation
Decision Automation focuses specifically on automating the decision step within a workflow, not just the surrounding tasks. The shift from automating tasks to automating decisions is where modern AI delivers exponential value.