A company has historic property data in CSV on S3 with a header, categorical fields, and missing values. Data scientists filled missing values with zeros, dropped categorical fields, and trained a default open-source linear regression model; prediction accuracy is under 50%. They want to improve model performance and launch quickly with minimal operational overhead. Which option best meets this goal?

The correct answer is to use SageMaker Autopilot. Autopilot automates feature engineering, algorithm selection, and hyperparameter tuning, significantly improving model performance over a default linear regression model with minimal effort. This directly addresses the need for quick launch and minimal operational overhead. The data scientists' initial approach of filling missing values with zeros and dropping categorical fields is suboptimal; Autopilot handles these aspects more effectively. Incorrect options: Using ECS with Deep Learning Containers requires significant manual effort for feature engineering, model selection, and operational management, which contradicts the goal of minimal operational overhead and quick launch. Launching a SageMaker notebook for interactive exploration is a good step for understanding data, but it doesn't automate the entire process of model building and deployment as effectively as Autopilot, nor does it guarantee the best model without extensive manual iteration. Using a SageMaker training job with XGBoost and then deploying to Lambda for inference involves more manual steps for model deployment and scaling compared to Autopilot's integrated deployment, and it still requires manual feature engineering and hyperparameter tuning for XGBoost to achieve optimal performance.