Here’s the translation to American English:
The evolution of machine learning has reached a point where it is no longer viewed solely as an experimental tool, but as an integral part of modern business operations. Companies are increasingly adopting machine learning models to carry out sales forecasts, customer segmentations, and churn predictions. Although traditional machine learning continues to demonstrate significant utility, the rise of generative artificial intelligence has begun to change how customer experience is established, providing more accessible and versatile tools.
Despite the boom in generative artificial intelligence, traditional machine learning methodologies are fundamental for certain predictive tasks. For example, sales forecasting, which relies on the analysis of historical data and trend studies, is best managed with established algorithms such as random forests, gradient boosting machines, ARIMA models, and LSTM networks. Similarly, traditional techniques like K-means segmentation are effective for classifying customers and predicting churn rates. While generative AI excels in creative areas like content production, traditional machine learning models still lead in data-driven predictions. The fusion of both approaches could result in more comprehensive solutions that provide accurate forecasts without sacrificing cost efficiency.
To enhance the capabilities of artificial intelligence agents, a workflow has been implemented that allows these systems to make informed decisions. This innovation is based on the use of the Model Context Protocol (MCP), a standard that facilitates how applications provide context to language models. Access to these technologies is enabled through Amazon SageMaker AI, which serves as a supporting platform.
Artificial intelligence agents built using the Strands Agents SDK leverage a large language model as their core, allowing them to observe their environment, plan actions, and carry out tasks with minimal human intervention. This capability goes beyond mere text generation, positioning the agents as autonomous entities that can act and make decisions in complex business environments.
The process for implementing this solution initially involves training a time series forecasting model using Amazon SageMaker AI. After proper handling of data and features, a model like XGBoost is trained to forecast future demands based on historical patterns. The model is then deployed to a SageMaker AI endpoint, enabling real-time access to its predictions through API calls.
The generated predictions are reintroduced to the agent, which can use them to enhance its decision-making and execute informed actions. This integrated architecture provides a solid foundation for artificial intelligence applications, offering both flexibility and the option to choose between direct access to endpoints or an MCP-based integration, thereby adapting to various business requirements.
Companies continue to explore methods to make their artificial intelligence agents smarter and more data-driven, and the combination of Amazon SageMaker AI, MCP, and the Strands Agents SDK emerges as an innovative solution to create next-generation applications.
Referrer: MiMub in Spanish
