Myth: MLOps is merely about automating tasks, thus saving time and labor for data scientists and engineers.
Reality: MLOps is not just about automating repetitive tasks; it's about democratizing access to machine learning (ML) capabilities, enabling collaboration, and ensuring high-quality ML models in production. While automation is a crucial aspect, its primary goal is to bridge the gap between research and production, making ML more reliable and scalable.
This misconception may stem from the early days of MLOps, when tooling and automation primarily focused on saving time and resources. However, as MLOps has evolved, it has become clear that its true value lies in providing a cohesive framework for deploying ML models that are robust, explainable, and aligned with business objectives. By doing so, MLOps facilitates the integration of ML into the overall organizational workflow, enabling data-driven decision-making and driving business growth.
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