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MLOps Lifecycle

Updated May 12, 2023 ·

Overview​

MLOps follows a structured process to turn machine learning ideas into real-world solutions.

  • Design: Define goals, assess data, and set success metrics.
  • Development: Train, test, and refine models.
  • Deployment: Integrate models, monitor performance, and update as needed.

A structured lifecycle presents a clear roadmap which helps plan each stage of execution and ensures the right people and tools are involved at the right time.

Design Phase​

This phase sets the foundation by defining goals and assessing feasibility.

  • Define the Problem

    • Identify business needs and success criteria.
    • Engage stakeholders to ensure project alignment.

  • Prepare Data

    • Collect and clean high-quality data.
    • Establish key metrics for evaluation.

Development Phase​

The model is built, tested, and improved.

  • Experiment & Train

    • Test different algorithms and parameters.
    • Train multiple models to compare performance.

  • Refine & Optimize

    • Evaluate results and fine-tune the model.
    • Ensure the model meets defined success criteria.

Deployment Phase​

The model is integrated into a live system and monitored.

  • Deploy & Scale

    • Package the model as a microservice or API.
    • Ensure smooth integration with existing systems.

  • Monitor & Maintain

    • Track performance and detect issues like data drift.
    • Update the model as needed to maintain accuracy.

Continuous Improvement​

MLOps is an ongoing process, not a one-time task.

  • Evaluate Regularly

    • Assess if the model is still delivering value.
    • Make adjustments based on real-world feedback.

  • Iterate & Improve

    • Refine models using new data and better techniques.
    • Adapt strategies to meet evolving business needs.