MLOps: Explanation and How It Works

As we know, Deep Learning and Machine Learning are part of AI, and the demand for AI has grown significantly as businesses rely on it to execute their projects.

In the middle of rising interest in AI implementation, a new term has emerged: MLOps. In this article, KLIK Group explains what MLOps is and how MLOps works.

Understanding MLOps

MLOps isn’t actually a new concept—the term is adapted from a development methodology called DevOps.

Read More - DevOps 101: Explanation & Its Role in Digital Transformation

What is DevOps?

In short, DevOps is a combination of "Development" and "Operations," referring to a methodology that emphasizes communication and collaboration among project developers. This methodology covers both the development and operational phases of applications.

The primary goal of DevOps is to enable companies to develop and deploy products faster through a continuous process and a collaboration-driven culture.

What is MLOps?

Building on the DevOps concept, MLOps (Machine Learning Operations) extends
DevOps principles and practices specifically to the field of machine learning. Hence, the "Dev" in DevOps is replaced with "ML" to highlight its focus on machine learning processes and to bridge the workflow between ML teams and operations teams.

By understanding the goals of DevOps, we can see that MLOps aims to ensure that models developed by ML teams are deployed efficiently, quickly, and in a continuous development cycle.

MLOps Life Cycle

The MLOps life cycle is a structured methodology for managing machine learning (ML) in production environments.

Stages of the MLOps Life Cycle:

• Model Development
  Building ML models based on business needs and data.
• Model Evaluation & Experimentation
  Comparing model performance and testing with relevant datasets.
• Productionizing Model
  Preparing the model for deployment and integration.
• Testing
  Validating performance, scalability, and security before deployment.
• Deployment
  Releasing the ML model into a production environment.
• Monitoring and Observatibility
  Tracking the performance of deployed ML models.

This cycle is iterative and continuous, ensuring that ML models remain effective, scalable, and up-to-date in production.

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