Intelligent Deployment Pipelines via MLOps
DOI:
https://doi.org/10.5281/zenodo.21455297Keywords:
MLOps, Intelligent Systems, ML Pipelines, Model Deployment, Model Lifecycle, AutoML, Human Loop, Decision Making, Risk Functions, Data Platforms, Pipeline Automation, Continuous Learning, Model Training, Model Tuning, Data Privacy, Model Security, Bias Control, Explainability, Scalability, Cost Optimization.Abstract
MLOps-enabled intelligent systems automate the deployment of machine learning pipelines that can exploit machine learning capabilities and pre-existing data across data platforms in multiple sectors, enabling automated decision-making with a known (soft) risk or loss function. MLOps concepts enable new forms of machine learning model lifecycle management that may leverage autoML and human-in-the-loop approaches for model development, training, and tuning; they facilitate the continuous operation of pipelines on a 24/7 basis; they address concerns such as privacy, security, model bias, transparency, explainability, interpretability, and inclusivity; and they provide mechanisms for automatic scaling, fault-tolerance, and infrastructure cost optimisation.
Automated model deployment represents a major feature of MLOps. Automated deployment refers to the management of machine learning models and pipelines so that the entire lifecycle from development to deployment and operation may be governed from an MLOps perspective. When system performance is measured within a known range and against a soft loss or risk function, virtual data platforms that encompass sectors as diverse as finance, healthcare, retail, and telecommunications possess feature sets supporting such automations. Such automations focus on Machine Learning as a Service (MLaaS) deployments on a 24/7 basis, with the objective of enabling decision making within these soft constraints and scaling the deployment of autoML pipelines in tandem with demand for untrained services.
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