MLOps
MLOps, or Machine Learning Operations, is a set of practices that aim to streamline the entire lifecycle of machine learning models, from development to deployment and maintenance. It involves automating and standardizing various tasks, ensuring efficient collaboration between data scientists, engineers, and other stakeholders.
- Data Management: Effective management of data pipelines, including data collection, cleaning, and preparation.
- Model Development: Building, training, and evaluating machine learning models using appropriate algorithms and techniques.
- Model Deployment: Deploying trained models into production environments for real-world use.
- Monitoring and Evaluation: Continuously monitoring model performance, detecting anomalies, and retraining models as needed.
MLOps Tools and Technologies
- Data Management: Apache Airflow, Luigi, Prefect
- Model Development: TensorFlow, PyTorch, Keras
- Model Deployment: Kubernetes, Docker, AWS SageMaker
- Monitoring: Prometheus, Grafana, MLflow
- Automation: Jenkins, CircleCI, GitLab CI/CD