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MLOps Pipeline

MLOps, or Machine Learning Operations, brings together processes, best practices, and technologies to manage putting machine learning models into production environments at scale. It fills a major gap enterprises face in getting return from AI and analytics investments.

Research shows only 15% of major companies have widespread machine learning applications running across their business. So the majority of expensive modeling work stays stuck in labs and pilot projects. MLOps fixes this bottleneck by automating the steps needed to deploy, monitor, and update models in reliable pipelines.

Key business benefits MLOps delivers includes:

Without MLOps, models degrade, data science productivity drops, and adoption stalls. Adding MLOps boosts ROI on analytics spending by maintaining model performance post-deployment.

MLOps engineers build the continuous development and deployment capabilities for machine learning models to run successfully as applications. Their expertise combines software engineering, data engineering, and DevOps skills tailored for operationalizing analytics.

Their key responsibilities include:

The rest is here:
Operationalizing Machine Learning to Drive Business Value | by dparente | Daniel Parente | Feb, 2024 - Medium