Exploring Open-Source Alternatives to Landing AI for Robust MLOps
· 7 min read
Photo by Luke Southern on Unsplash
In the thriving world of IoT, integrating MLOps for Edge AI is important for creating intelligent, autonomous devices that are not only efficient but also trustworthy and manageable.
MLOps—or Machine Learning Operations—is a multidisciplinary field that mixes machine learning, data engineering, and DevOps to streamline the lifecycle of AI models.
In this field, important factors to consider are:
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explainability, ensuring that decisions made by AI are interpretable by humans;
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orchestration, which involves managing the various components of machine learning in production–at scale; and
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reproducibility, guaranteeing consistent results across different environments or experiments.