MLOps (machine learning operations) is the practice of building, deploying, monitoring and retraining machine learning models reliably in production. It applies DevOps ideas — version control, automated testing, CI/CD and monitoring — to data, features and models.
Typical components are experiment tracking, a model registry, reproducible training pipelines, automated deployment and monitoring for data drift and model performance.
Read more about MLOps
- How We Productionise Data and AI on Databricks: Bundles, CI/CD, Testing, MLOps and OperationsOur playbook from notebook to production: repo layout, Asset Bundles, CI/CD with service principals, data testing, idempotent backfills, MLflow and operations.
- A Field Guide to Machine Learning Algorithms: Assumptions, Failure Modes and When We Use EachLinear and logistic models, k-NN, SVMs, random forests, boosting, clustering, anomaly detection, forecasting and recommenders — what each assumes and how each fails.
How Marquedata helps
Data Science & MLData science, machine learning, model training and LLM fine-tuning — from experiment to monitored production model.
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