Description

Version control is a fundamental aspect of MLOps, ensuring the traceability and reproducibility of code and data changes throughout the machine learning lifecycle.

Continuous integration and continuous deployment (CI/ CD) pipelines streamline the process of building, testing, and deploying machine learning models.

Model registry and management tools centralise the storage and organisation of trained machine learning models, facilitating discovery, sharing, and deployment.

Monitoring and logging solutions are crucial for maintaining the health and performance of machine learning models in production.

Implementing MLOps in practice: A quick guide

Getting MLOps up and running doesn’t have to be complicated. Here is a concise guide to help you navigate the key steps in implementing MLOps within your organisation.

Step 1. Assess your current ML workflow

Main objective

● Gain a comprehensive understanding of your existing machine learning (ML) workflow to identify strengths, weaknesses, and areas for improvement.

Action steps

Outline your current ML processes — from data preparation to model deployment.

● Engage with stakeholders to pinpoint challenges and bottlenecks.

Establish metrics for model training times, deployment frequency, and overall efficiency.

Evaluate the effectiveness of communication and feedback mechanisms between teams.

Step 2. Define MLOps processes for your team

Main objective

● Develop clear MLOps processes to ensure collaboration,

accountability, and efficiency within your team.

Action steps

● Clearly define the roles and responsibilities of data scientists, ML engineers, and operations.

Document end-to-end workflows for model development, testing, deployment, and monitoring.

Set up governance policies for version control, data