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  photo: pexel kevin blenzy Understanding MLOps MLOps (Machine Learning Operations) is the practice of deploying and maintaining machine learning models in production. It involves a systematic approach to the entire machine learning lifecycle, from data ingestion and preparation to model training, deployment, monitoring, and retraining. MLOps Lifecycle The MLOps lifecycle typically consists of the following stages: Data Ingestion: Acquiring and loading data from various sources. Data Preparation: Cleaning, transforming, and preparing data for modeling. Model Training: Building and training machine learning models. Model Evaluation: Assessing model performance using appropriate metrics. Model Deployment: Integrating the model into production systems. Model Monitoring: Tracking model performance in production and detecting issues. Model Retraining: Updating models based on new data or performance degradation. MLOps with Snowflake ML and Kubeflow Let's explore how Snowfl...