Machine learning operations (MLOps) is the combination of people, processes, and technology to productionize ML use cases efficiently. To achieve this, enterprise customers must develop MLOps platforms to support reproducibility, robustness, and end-to-end observability of the ML use case’s lifecycle. Those platforms are based on a multi-account setup by adoptingContinue Reading
Retrieval Augmented Generation (RAG) is a powerful approach for building generative AI applications by providing foundation models (FMs) access to additional, relevant data. This approach improves response accuracy and transparency while avoiding the potential cost and complexity of FM training or fine-tuning. Many customers use Amazon Bedrock Knowledge Bases toContinue Reading
Extracting meaningful insights from unstructured data presents significant challenges for many organizations. Meeting recordings, customer interactions, and interviews contain invaluable business intelligence that remains largely inaccessible due to the prohibitive time and resource costs of manual review. Organizations frequently struggle to efficiently capture and use key information from these interactions,Continue Reading
Amazon SageMaker Projects empower data scientists to self-serve Amazon Web Services (AWS) tooling and infrastructure to organize all entities of the machine learning (ML) lifecycle, and further enable organizations to standardize and constrain the resources available to their data science teams in pre-packaged templates. For AWS customers using Terraform to defineContinue Reading
