MLOps

Machine Learning Operations

1. Overview of MLOps — Optimizing Lifecycle Management for AI Models

    flowchart LR
    A["Experiment-centric<br/>ML development"] -- "Shift to continuous<br/>deployment & operational<br/>automation" --> B["MLOps system"]

Definition: A methodology and system that integrates machine-learning development (ML) and operations (Ops), automating the entire process of model training, deployment, and monitoring while ensuring reliability.

Characteristics: (Continuous training) Automates model retraining on new data through Continuous Training (CT). (Version control) Version-controls data, features, models, and code together to ensure reproducibility and governance. (Drift detection) Monitors model-performance drift in real time and triggers automatic retraining when quality degrades.

2. The MLOps Pipeline and Maturity Model

A. The Core MLOps Workflow (End-to-End Pipeline)

    flowchart LR
    DATA["Data Prep<br/>(Collection/processing)"]
    TRAIN["Model Training<br/>(Experimentation/training)"]
    VAL["Validation<br/>(Evaluation/verification)"]
    REG["Model Registry<br/>(Model store)"]
    DEP["Serving<br/>(Deployment/inference)"]
    MON["Monitoring<br/>(Performance tracking)"]

    DATA --> TRAIN --> VAL --> REG --> DEP --> MON
    MON -- "On degraded performance" --> DATA
    
    style REG fill:#1E3A5F,color:#fff

Stage	Key Activities	Key Tools (examples)
Data Prep	Feature engineering, data version control	Feast, DVC, Spark
Training	Hyperparameter tuning, experiment tracking	MLflow, Kubeflow, WandB
Serving	Deploying models as APIs (online/batch)	Seldon Core, TF Serving, BentoML
Monitoring	Detecting data/model drift	Prometheus, Grafana, Evidently

B. An Evolutionary Perspective on MLOps

    flowchart LR
    subgraph A["Experiment-centric model development"]
        A1["Manual model training<br/>(fragmented pipelines)"]
    end

    subgraph TRANS["Evolution"]
        direction TB
        T1["Pipeline automation (CI/CD)"]
        T2["Continuous training (CT)"]
    end

    subgraph B["MLOps operating system"]
        B1["Model lifecycle management<br/>(operational stability/reliability)"]
    end

    A -- "Overcoming the limits of<br/>model development" --> TRANS
    TRANS --> B

Comparison	DevOps (SW)	MLOps (ML)
Core asset	Code	Code + data + model
Version control	Source-code versions	Code + hyperparameter + dataset versions
Deployment success criteria	Successful build and passing tests	Passing metrics such as precision and recall
Continuous elements	CI / CD	CI / CD / CT (Continuous Training)

3. Expected Benefits and Success Strategy for MLOps

Category	Key Benefit	Application in Practice
Operational stability	Sustained AI model performance over time	Immediate response to performance degradation through real-time monitoring
Improved productivity	Faster experimentation and shorter deployment cycles	Streamlining development through templated model pipelines
Stronger governance	Traceability and history of model changes	Ensuring transparency in the model-creation process to support AI regulatory compliance
Maximizing value	Higher success rate turning AI experiments into business services	Realizing AI value in real production environments, beyond PoC

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