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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.<br>

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

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

  2. Understanding MLOps 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. iabac.org

  3. Key Components of MLOps 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. iabac.org

  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. iabac.org

  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. iabac.org

  6. Thank You visit: www.iabac.org iabac.org

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