MLOps tutorials

Open-source tutorials on MLOps, ML model evaluation, and monitoring for data scientists and machine learning engineers. All tutorials contain end-to-end code examples.

Batch inference and ML monitoring with Evidently and Prefect

In this tutorial, you will learn how to run batch ML model inference and deploy a model monitoring dashboard for production ML models using open-source tools.

How to stop worrying and start monitoring your ML models: a step-by-step guide

A beginner-friendly MLOps tutorial on how to evaluate ML data quality, data drift, model performance in production, and track them all over time using open-source tools.

Monitoring unstructured data for LLM and NLP with text descriptors

How do you monitor unstructured text data? In this code tutorial, we’ll explore how to track interpretable text descriptors that help assign specific properties to every text.

A simple way to create ML Model Cards in Python

In this code tutorial, you will learn how to create interactive visual ML model cards to document your models and data using Evidently, an open-source Python library.

ML serving and monitoring with FastAPI and Evidently

In this code tutorial, you will learn how to set up an ML monitoring system for models deployed with FastAPI. This is a complete deployment blueprint for ML serving and monitoring using open-source tools.

Batch ML monitoring blueprint: Evidently, Prefect, PostgreSQL, and Grafana

In this code tutorial, you will learn how to run batch ML model inference, collect data and ML model quality monitoring metrics, and visualize them on a live dashboard.

A tutorial on building ML and data monitoring dashboards with Evidently and Streamlit

In this tutorial, you will learn how to create a data quality and ML model monitoring dashboard using the two open-source libraries: Evidently and Streamlit.

Monitoring NLP models in production: a tutorial on detecting drift in text data

In this tutorial, we will explore issues affecting the performance of NLP models in production, imitate them on an example toy dataset, and show how to monitor and debug them.

How to detect, evaluate and visualize historical drifts in the data

You can look at historical drift in data to understand how your data changes and choose the monitoring thresholds. Here is an example with Evidently, Plotly, Mlflow, and some Python code.

What Is Your Model Hiding? A Tutorial on Evaluating ML Models

There is more to performance than accuracy. In this tutorial, we explore how to evaluate the behavior of a classification model before production use.

How to break a model in 20 days. A tutorial on production model analytics

What can go wrong with ML model in production? Here is a story of how we trained a model, simulated deployment, and analyzed its gradual decay.

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Open-source ML observability course

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