Machine Learning (ML) model management refers to the process of organizing, monitoring, and maintaining ML models throughout their lifecycle. It involves managing and orchestrating every aspect of ML models, from their creation, testing, validation, and deployment stages, also extending to their monitoring and retraining.
It is crucial for ensuring the models' efficiency, accuracy, and reliability in producing the desired outcomes.
ML Model Management in practice
ML model management works in a systematic and thorough way to ensure the highest performance of ML models.
The process begins with the creation of ML models, wherein data scientists use different algorithms and techniques to build models. These models are then tested and validated using various datasets to assess their accuracy and performance. Implemented version control helps track the evolution of the models.
Once a model is ready, it is deployed for real-world use. The ML model management oversees this deployment to assess if the model works as expected in a production environment, and helps with scaling the model to handle larger datasets or more complex computations.
Post-deployment, the ML model management continues to monitor the models’ performance and make necessary adjustments. Continuous monitoring is required as models may degrade over time due to changes in data or the environment it’s used in. Based on the monitoring results, models may require retraining with new data or adjustments in their parameters.
ML model management also involves documentation and interpretation of the model for stakeholders' understanding, and it ensures model compliance with industry regulations and standards. In this way, ML model management provides a structured way to manage the end-to-end lifecycle of ML models efficiently.
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