What's included

• Data Preparation and Preprocessing

  Preparing and preprocessing datasets to ensure they are in the optimal format for training and deployment in Amazon SageMaker.

• Model Training and Optimization

  Training machine learning models with high accuracy and performance using Amazon SageMaker’s built-in algorithms and optimization techniques.

• Hyperparameter Tuning

  Conducting hyperparameter tuning using Amazon SageMaker’s automatic model tuning feature to improve model performance.

• Model Deployment as Endpoints

  Deploying trained models as scalable endpoints in Amazon SageMaker to enable real-time predictions and inference.

• Custom Algorithm Deployment

  Deploying custom-trained models and algorithms on Amazon SageMaker tailored to specific business requirements.

• Integration with AWS Ecosystem

  Integrating deployed models with other AWS services like Lambda, S3, and API Gateway for seamless data flow and application support.

• Automated Deployment Pipelines

  Setting up CI/CD pipelines for automated deployment and updates of machine learning models using Amazon SageMaker.