Sure! Here’s the translation to American English:
Recent advancements in generative artificial intelligence are transforming natural language processing operations across various organizations. However, developers and data scientists face significant challenges in customizing these large models. Among the main complications are managing complex workflows, effectively preparing large datasets for fine-tuning, implementing techniques for optimizing computational resources, and continuously monitoring model performance, as well as achieving scalable and reliable deployments. This fragmentation in tasks can hinder productivity and prolong development times, creating potential inconsistencies in the model production pipeline.
To address these challenges, Amazon Web Services (AWS) has expanded the capabilities of Amazon SageMaker by introducing a comprehensive set of tools for data, analytics, and generative AI. At the center of this evolution is Amazon SageMaker Unified Studio, a centralized service that operates as an integrated development environment (IDE). This new studio optimizes access to tools and features from analytics and machine learning services such as Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, and Amazon Bedrock.
SageMaker Unified Studio allows users to discover data through Amazon SageMaker Catalog and access it from Amazon SageMaker Lakehouse. It also facilitates the selection of base models from Amazon SageMaker JumpStart or their construction via JupyterLab, providing the necessary capabilities to train and fine-tune them using the SageMaker AI training infrastructure. The latter is a fully-managed service that simplifies the building, training, and deployment of machine learning models.
Platform users can customize large language models, following a framework that spans from data discovery to fine-tuning and metric tracking for real-time deployments. Best practices are also provided regarding instance selection and debugging strategies while working in JupyterLab.
The process involves multiple steps, starting with setting up a domain in SageMaker Unified Studio, managing connections and permissions, creating projects within the IDE, and managing extraction, transformation, and loading (ETL) pipelines in a single environment. This enables data engineers to manipulate and transform datasets for efficient exploratory analysis. A notable feature is the inclusion of tools like MLflow for monitoring experiments, ensuring clear metrics and results regarding model training.
Finally, deployment is optimized through real-time inference strategies, using specific instances for models that provide complete control over inference resources. SageMaker Unified Studio establishes itself as a robust solution that simplifies complex workflows in artificial intelligence, facilitating a smooth transition from preparation to effective and scalable deployment of machine learning models.
Referrer: MiMub in Spanish