Choosing the Right MLOps Platform: Key Capabilities & Model Monitoring Insights

As the field of machine learning evolves, the operationalization and monitoring of intelligent models in production, also known as machine learning operations (MLOps), has become essential. Companies are increasingly adopting artificial intelligence (AI) as a strategic resource, thus increasing the demand for reliable and scalable MLOps platforms.
Therefore, all phases of the machine learning life cycle, from data preparation, orchestration of workflows to monitoring of performance, present challenges and opportunities that demand state-of-the-art, flexible, and scalable technical solutions.

Recently, we published a research [1] that addresses this demand by providing a comprehensive assessment. In this contribution, we present a three-step evaluation framework for 16 MLOps platforms we reviewed.
The framework integrates tools capability analysis, GitHub stars growth evaluation, and weighted scoring method to assist organizations in identifying the most appropriate MLOps platform that fits their requirements.
We highlight essential capabilities such as experiment tracking, model development, and orchestration that are critical for building scalable AI workflows.
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Additionally, in this presentation, we analyse and compare model performance monitoring tools offering insights into their effectiveness and suitability for different MLOps environments.