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Introduction

Updated May 12, 2023 ·

Overview​

MLOps, or Machine Learning Operations, is a framework for deploying and maintaining machine learning models in production environments.

  • For real-world use

    • Models are part of business processes, not just local experiments.
    • Focuses on deployment, not just training.

  • Covers the full process

    • Includes design, development, deployment, and maintenance.
    • Ensures models stay accurate and reliable.

Benefits of MLOps​

MLOps makes ML faster and more reliable.

  • Improves teamwork

    • Bridges the gap between ML and operations teams.
    • Enhances communication and collaboration.

  • Automates deployment

    • Reduces manual work and errors.
    • Speeds up model releases.

  • Tracks performance

    • Detects performance drift and issues.
    • Ensures models stay accurate in production.

Origin of MLOps​

MLOps comes from DevOps, which improves software development.

  • Based on DevOps

    • DevOps automates coding, testing, and deployment.
    • Improves software reliability and speed.

  • Applied to ML

    • Data scientists and ML engineers collaborate.
    • Streamlines model deployment and monitoring.

More Than Just Models​

Building a machine learning model is just one part of the process.

  • Data preparation

    • Ensures high-quality, clean data for training.
    • Uses feature engineering to create useful inputs.

  • Performance analysis

    • Analyzes performance before deployment.
    • Detects issues early to improve accuracy.

  • Continuous monitoring

    • Tracks model performance over time.
    • Adjusts models to maintain reliability.

Roles in MLOps​

The ML lifecycle follows a structured process with different tasks handled by various roles. These roles fall into two categories: business and technical.

Business Roles​

Business roles focus on project goals, budget, and domain knowledge.

  • Business Stakeholder

    • Oversees budget and project alignment with company goals.
    • Defines business requirements and evaluates project success.

  • Subject Matter Expert

    • Provides industry-specific knowledge.
    • Helps interpret data and model results.

Technical Roles​

Technical roles handle data, model development, and deployment.

  • Data Scientist

    • Analyzes data, trains models, and evaluates performance.
    • Monitors deployed models to ensure accuracy.

  • Data Engineer

    • Collects, stores, and processes data.
    • Ensures data quality through validation and testing.

  • ML Engineer

    • Works across all ML lifecycle stages.
    • Manages data extraction, model training, and deployment.

Other Roles​

Additional contributors vary based on the project and company structure.

  • Software Engineers & Backend Engineers

    • Assist with integrating models into applications.

  • Data Analysts

    • Provide insights from data for better decision-making.

  • Developers

    • Help with coding, automation, and infrastructure setup.

Roles in startups may be more flexible, while enterprises typically have specialized teams.