Guided series
MLOps: Ship Models to Production
The full path from a notebook to a monitored, tested, automated ML system — CI/CD, orchestration, data quality, drift detection and beyond.
10 articles · in reading order
-
MLOps for Beginners: Complete Guide to Production Machine Learning
Complete MLOps beginner guide — experiment tracking, data versioning, model serving, monitoring, and avoiding tool-sprawl in production machine learning systems.
September 7, 2026 -
CI/CD for Machine Learning: Automate Model Testing and Deployment (2026)
Build production ML pipelines with GitHub Actions, CML, and quality gates — catch regressions before they reach users.
September 8, 2026 -
Docker Compose for ML Projects: Multi-Container Development Setup
Docker Compose for ML projects: set up GPU-enabled multi-container development with health checks, watch mode, profiles, and resource limits.
September 30, 2026 -
Apache Airflow Tutorial: Orchestrate Your ML Workflows (2026)
Learn to orchestrate ML pipelines with Apache Airflow 3.3.1 using TaskFlow API, DAGs, and best practices for production-grade workflows.
September 8, 2026 -
dbt Tutorial: Transform Data for Machine Learning Pipelines (2026)
Master dbt for ML feature engineering — version-controlled SQL transformations, train-serve skew prevention, and production pipeline patterns.
September 8, 2026 -
Data Versioning with DVC: Track Datasets Like Code (2026)
Master DVC for data versioning — track datasets, models, and ML experiments using Git without bloating your repository.
September 8, 2026 -
Data Validation with Great Expectations: Catch Bad Data Early (2026)
Validate data quality with Great Expectations GX Core — write Expectations, build Suites, and catch bad data before it corrupts ML pipelines.
September 8, 2026 -
Model Monitoring in Production: Detect Drift Before It Hurts You (2026)
Detect data drift, concept drift, and prediction drift in production ML models using Evidently AI, alerts, and continuous monitoring pipelines.
September 8, 2026 -
A/B Testing ML Models in Production: Canary and Shadow Deployments (2026)
Four production validation strategies for ML models — shadow, canary, A/B testing, and interleaved — with the statistical reasoning behind each.
September 9, 2026 -
SageMaker Pipelines Tutorial: End-to-End MLOps on AWS
Build reproducible, auditable ML workflows on AWS: SageMaker Pipelines tutorial covering the @step decorator, step classes, and FailStep gates.
September 30, 2026
Want more paths? Browse all series or pick a topic.