What Is MLOps and How Does It Work in AI Projects | IABAC

MLOps combines machine learning and DevOps practices to streamline the development, deployment, and management of AI models. It automates workflows, enhances collaboration between teams, ensures model reliability, and enables continuous integration, delivery, and monitoring throughout the machine learning lifecycle.

1. What Is MLOps and
How Does It Work in
AI Projects? iabac.org‌

2. MLOps stands for Machine Learning Operations.‌
It combines machine learning (ML) with DevOps
principles.‌
Focuses on automating ML model development,‌
‌deployment, and monitoring.‌
Goal: streamline collaboration between data scientists‌
‌and IT teams.‌
Understanding MLOps
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3. Data Management: Collecting, cleaning, and versioning datasets.‌
Model Development: Building and training models efficiently.‌
Model Deployment: Automating release into production.‌
Monitoring & Maintenance: Tracking model performance and drift.‌
Continuous Integration & Delivery (CI/CD): Ensuring reliable‌
‌updates.‌
Key Components of MLOps
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4. How MLOps Works in AI Projects
Model Creation: Data scientists experiment and train models.‌
Pipeline Automation: MLOps automates testing and validation.‌
Deployment: Models are integrated into production systems.‌
Monitoring: Performance metrics and drift are continuously
tracked.‌
Iteration: Models are retrained as new data arrives.‌
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5. Benefits and Outcomes
Faster deployment of AI models.‌
Improved collaboration across teams.‌
Consistent model performance in production.‌
Scalable and repeatable ML workflows.‌
Better governance and traceability of AI systems.‌
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6. Thank You visit: www.iabac.org iabac.org‌