Skill

ML Model Deployment

Data Science, Analytics and AI/ML

ML model deployment is the process of taking a trained machine learning model and making it available for use in a production environment, such as via an API, batch job, or embedded application. It involves packaging the model, managing dependencies, ensuring scalability and low latency, and integrating it into existing software systems. ML engineers and MLOps practitioners handle deployment using tools like Docker, Kubernetes, and cloud services such as AWS SageMaker or Vertex AI.

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• CI/CD
• Kubernetes
• Docker
• MLOps
• AWS SageMaker