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MLOps (Machine Learning Operation) refers to a set of best practices, tools and technologies that are used to streamline and manage the lifecycle of machine learning models. It combines machine learning (ML) with DevOps (Development and Operations) principles to create a consistent and automated workflow for developing, deploying, and maintaining ML models.

MLOps encompasses various stages of the ML model development lifecycle, including data preparation, model training, model deployment, monitoring, and retraining. It aims to address the challenges associated with ML model development, such as reproducibility, scalability, versioning, and collaboration.

Topic Subtopic
Desinging ML Systems MLOps Overview
Training System Vs. Inference System
Prediction Patterns
Data Preparation Active Learning
Weak Supervision
Model Development Model Baseline

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