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Machine Learning Operations MLOps Overview, Definition And Architecture

Blog Author: Vidhi Gupta

Published: May 9th, 2024

Read Time: 3:00 Minutes

1. MLOps Overview

2. MLOps Definition

3. MLOps Architecture

1. Data Management -

2. Model Development -

3. Model Deployment -

4. Modeling & Logging -

5. Model Governance & Compliance -

4. Wrap-Up

Machine learning (ML) projects are developed and deployed to cure industrial business problems. However, the biggest difficulty faced by organizations these days is automating the ML pipeline. To battle this issue, MLOps or machine learning operations came into existence.

This blog taps into machine learning operations (MLOps) overview, definition and architecture. This will help beginners get a better idea about this technology and how it's useful for organizations.

MLOps Overview

Machine learning aids companies in analyzing data and deriving valuable insights to push better decision-making. In spite of being a highly useful technology, it does not come without its own unique set of challenges and issues. Right control and guidance are needed to ensure that data science teams get their desired result.

To tackle these challenges, MLOps came into existence. This practice is a guiding map to steer ML projects towards success, irrespective of the constraints. There are many benefits of implementing MLOps including-

Enhanced productivity
Faster time to market
Better model deployment

MLOps Definition

The term comes by amalgamating the terms machine learning (ML) and operations (Ops). This set of management practices have become vital to ensure proper deep learning (DL) or production ML lifecycles. Just like DevOps, it is also neither a technology nor a tool.

It's a practice that entails the optimum utilization of various such aids available in the market. The goal is to reliably and efficiently deploy ML models in production as well as maintain them. Various DevOps, data engineering and ML processes are employed to make it happen. Uninterrupted collaboration and communication is promoted between data scientists and operations professionals.

This approach shares quite a lot of similarities with DevOps, DataOps and ModelOps. Its focus is to enhance the quality of production ML and expand automation to maintain attention on business and regulatory requirements. In recent years, it has experienced dramatic changes and it's still evolving.

You May Also Read - MLOps Interview Questions and Answers

MLOps Architecture

MLOps architecture refers to a comprehensive framework comprising different processes and components of the ML pipeline.

MLOps Architecture

MLOps architecture includes these key elements.

1. Data Management -

It entails collecting, versioning and preprocessing data to ensure only high-quality data is extracted for model training & testing.

2. Model Development -

It entails tasks like model training, versioning and evaluation. The team must assess model performance, tune hyperparameters and select apt algorithms.

3. Model Deployment -

It entails setting up CI/CD pipelines, packaging ML models into deployment or containers artifacts and orchestrating deployment.

4. Modeling & Logging -

It enables capturing performance metrics, monitoring deployed models in real-time and logging associated predictions. It helps data science teams in detecting issues, debugging them and checking performance optimization.

5. Model Governance & Compliance -

It guarantees models' adherence to ethical and regulatory guidelines. It incorporates documentation, version control and maintaining security standards and data privacy.

Wrap-Up

Every organization that aims at succeeding finds it imperative to implement MLOps best practices in their working. Hence, anyone who masters this practice and changes their skills with the changes is sure to enjoy a bright future in this career space.

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