Machine Learning Model Deployment

Data scientists often focus on building models, but ML engineers need to know how to deploy these models into production environments. Skills in containerization tools like Docker and orchestration platforms such as Kubernetes are essential. Learning deployment frameworks (e.g., TensorFlow Serving, TorchServe) and cloud services (AWS SageMaker, GCP AI Platform) will help ensure models are robust and scalable.