Skip to main content

Introduction

Purpose of the User Guide​

This document provides comprehensive guidance on the usage of the mlangles mlops platform, an enterprise-grade solution designed to manage the end-to-end machine learning lifecycle at scale.

It is intended for data scientists, machine learning engineers, data engineers, DevOps professionals, platform administrators, and other technical stakeholders responsible for the development, deployment, monitoring, and governance of machine learning workflows.

The purpose of this guide is to:

  • Facilitate onboarding by detailing how to access, configure, and navigate the platform.
  • Provide step-by-step instructions for building, training, deploying, monitoring, and managing machine learning models.
  • Reduce operational overhead by leveraging the platform’s automation, orchestration, and version control features.

This guide functions both as a procedural manual and as a reference for all user roles interacting with the MlanglesMlops Platform.

Scope of the Product Covered​

This guide applies to version 1.0.0 of the mlangles mlops platform, which supports scalable pipeline orchestration and model deployment for enterprise use cases. It focuses on the core capabilities available in the Enterprise Edition and assumes that users have access to the full feature set.

Included in Scope​

  • Project creation and user management
  • Configuration of access and function roles
  • Model training pipeline orchestration using the Pipeline module
  • Model training and experimentation via the Experiment module
  • Model registration, versioning, and deployment using ModelHub
  • Scheduling large-scale batch data for model inference using Batch Serving
  • Testing model predictions in real-time using individual data points via Online Serving