Building MLOps Pipeline for Time Series Prediction [Tutorial]

This tutorial provides a comprehensive guide to building an MLOps pipeline for a time series prediction project using a Bitcoin trading example on the Binance trading app. It follows MLOps best practices by integrating continuous integration, continuous delivery, continuous training, and continuous monitoring into the machine learning workflow. The tutorial outlines the steps necessary for design and scope, development, and operations phases, emphasizing the importance of understanding business goals, data engineering, exploratory data analysis, model development using tools like Optuna and XGBoost, and experiment tracking via neptune.ai. Automated testing is implemented using GitHub Actions, with deployment facilitated through Docker and AWS services, including ECS and ECR, ensuring seamless CI/CD. The tutorial also highlights the use of Neptune for monitoring model performance and data drift, providing a robust framework for managing the dynamic nature of machine learning projects in production environments.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.