MLOps, GitHub, repositories, learn, Electro4u, skill

Learn MLOps From These GitHub Repositories

MLOps (Machine Learning Operation) is an emerging practice that helps organizations optimize their machine learning production lifecycle. With MLOps, teams can build, deploy, monitor, and maintain machine learning models more efficiently at scale. By utilizing version control, data science pipelines, and real-time monitoring, teams can reduce errors and lessen the overall time to market with improved ML model performance.

If you’re looking to get started with MLOps and learn how to utilize these tools for your own projects, here are some great GitHub repositories to dive into:

mlflow

MLflow is an open source platform for managing the end-to-end Machine Learning lifecycle. MLflow’s mission is to make it easier for data scientists and machine learning engineers to manage ML experiments, package models and deploy them as APIs or microservices. MLflow enables users to track experiments, package models, reproduce runs, deploy models, and serve predictions.

seldon-core

Seldon Core is an open source machine learning platform for managing and orchestrating machine learning deployments. It allows users to deploy, manage, and operate complex ML models in production with high scalability, speed, and accuracy. It provides an API layer that abstracts away the complexity of deploying machine learning models and enables developers to quickly create, manage and monitor predictions.

alibi

Alibi is an open source toolkit for building machine learning models for outlier detection and anomaly detection. It provides a library of algorithms that can be used to detect outliers and anomalies in datasets. Additionally, Alibi includes tools for visualizing and explaining model predictions, monitoring and validating models, and automating model retraining.

clipper

Clipper is an open source framework for deploying and serving machine learning models in real-time. It simplifies model deployment and management by providing an API layer that abstracts away the underlying complexities. Clipper makes it easy to deploy and manage ML models in production and enables developers to quickly develop, deploy, and reuse ML models.

airflow

Apache Airflow is an open source platform for scheduling and orchestrating workflows. It provides an intuitive web UI for monitoring and managing machine learning pipelines, as well as a CLI for creating and managing workflows. Airflow makes it easy to schedule and execute tasks related to the ML lifecycle, such as data preprocessing, training, and deployment.

These are just a few of the many great open source MLOps tools and projects out there. If you’re interested in learning more about MLOps, check out these GitHub repositories to get started.