Machine Learning Operations
Machine Learning Operations (MLOps)
MLOps is the set of practices and tools that aim to streamline and standardize the development, deployment, monitoring, and management of machine learning (ML) models in production.
Key Components
- Model Development – Creating, training, validating models using TensorFlow, PyTorch, or Scikit-learn.
- Model Deployment – Serving trained models in production as APIs or embedded services.
- Model Monitoring and Maintenance – Tracking performance, detecting data drift, ensuring updates.
- Data Engineering – Preparing and pipelining data for training and inference.
- Automation – Automating repetitive tasks through CI/CD pipelines.
- Collaboration – Teamwork between data scientists, ML engineers, and DevOps professionals.
Key Practices
- Version Control (Git, DVC, MLflow)
- Continuous Integration/Continuous Deployment
- Model Lifecycle Management
- Reproducibility
- Scalability
- Data Governance
Popular Tools
- Versioning: Git, DVC, MLflow
- Tracking: Weights & Biases, Comet, TensorBoard
- Orchestration: Kubeflow, Apache Airflow, Prefect
- Deployment: Seldon, TensorFlow Serving, TorchServe
- Monitoring: Prometheus, Grafana, WhyLabs
FAQ
Machine Learning Operations (MLOps) is the set of practices and tools that standardize development, deployment, monitoring, and management of ML models in production. It blends DevOps, data engineering, and machine learning so models ship efficiently and run reliably at scale.