MLOps

MLOps (short for Machine Learning Operations) is a set of practices, tools, and processes that aim to automate and streamline the lifecycle of machine learning models, from development to deployment and monitoring — much like DevOps does for software engineering.

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🔧 MLOps = ML + DevOps

It combines:

• Machine Learning (ML): building and training models
• DevOps: automating software delivery and infrastructure changes

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🔁 Key Stages of the MLOps Lifecycle:

1. Model Development
   • Data preprocessing
   • Feature engineering
   • Model training & validation
   • Experiment tracking
2. Model Deployment
   • Packaging the model (e.g., with Docker)
   • Deploying to production (REST API, batch, streaming)
   • Versioning models
3. Model Monitoring & Maintenance
   • Monitoring performance (accuracy, drift, latency)
   • Retraining or rolling back as needed
   • Logging and alerts

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⚙️ MLOps Tools (Examples):

• Data versioning: DVC, LakeFS
• Model training: MLFlow, Kubeflow, SageMaker
• CI/CD pipelines: GitHub Actions, Jenkins, Argo Workflows
• Monitoring: Prometheus, Seldon Core, WhyLabs

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✅ Benefits of MLOps:

• Faster and more reliable ML deployment
• Reproducibility and auditability of experiments
• Continuous training and evaluation
• Scalability of ML systems
• Collaboration between data scientists and engineers