Introduction: MLOps, or Machine Learning Operations , is a set of practices that combines Machine Learning (ML) and DevOps to streamline and automate the end-to-end machine learning lifecycle. As machine learning models become more integral to business operations, MLOps ensures that they are deployed, managed, and maintained efficiently and effectively. Below are the top 20 important topics in MLOps: MLOps Training Course in Hyderabad 1. Introduction to MLOps MLOps is the practice of applying DevOps principles to machine learning workflows. It emphasizes automation, collaboration, and continuous integration/continuous deployment (CI/CD) to improve the reliability and scalability of ML models in production. 2. ML Lifecycle Management The ML lifecycle involves stages such as data collection, model training, validation, deployment, monitoring, and retraining. Effective lifecycle management ensures that models are updated and maintained over time, reflecting changes in data or requ...
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