What you can do with Amazon Sagemaker: Prepare data: SageMaker offers you tools to clean, transform and normalize your data, as well as to create training, validation and test sets. You can use the visual interface of SageMaker Studio or the Python SDK to prepare your data. Train models: SageMaker allows you to train machine learning models with a wide variety of algorithms, including supervised, unsupervised and reinforcement learning. Deploy models: SageMaker provides you with several ways to deploy your machine learning models in production Monitor models: SageMaker allows you to monitor the performance of your machine learning models in production. You can view metrics such as accuracy, recall and latency Evaluation: Automate the evaluation of trained models in Amazon SageMaker. Evaluates metrics such as precision, accuracy, F1-score, AUC-ROC and precision-recall curves. Supports different types of models, such as classification, regression, and anomaly detection. Generates detailed reports summarizing model performance. What you can do with Amazon Sagemaker and Pragma: Logistics: Predict delivery times, optimize routes, automate demand forecasting. Inventory: Manage stock levels, predict product demand fluctuations, prevent stock-outs. Financial product acquisition: Identify profitable investment opportunities, assess credit risk, customize financial product recommendations. Some benefits: Reduced costs: Eliminate the need for upfront investment in infrastructure and ongoing maintenance. Faster time to market: Streamline the ML development process with pre-defined tools and workflows. Scalability: Easily scale your ML models to meet changing demands. Enhanced security: Benefit from AWS's robust security infrastructure for your data and models.