Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect roles. GenAI most consistently supports (i) design ideation and trade-off exploration; (ii) rapid creation and refinement of artifacts (e.g., code, models, documentation); and (iii) architectural decision support and knowledge retrieval. Reported risks include opacity and bias, contextually incorrect outputs leading to rework, privacy and compliance concerns, and social loafing. We also identify emerging skills and competencies, including prompt engineering, model evaluation, and professional oversight, and organizational enablers around readiness and adaptive governance. The review contributes with (1) a mapping of GenAI use cases and risks in agile architecting, (2) implications for capability building and governance, and (3) an initial research agenda on human-AI collaboration in architecture. Overall, the findings inform responsible adoption of GenAI that accelerates digital transformation while safeguarding architectural integrity.

RStudio on Amazon SageMaker is the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. In conjunction with tools like RStudio on SageMaker, users are analyzing, transforming, and preparing large amounts of data as part of the data science and ML workflow. Data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for large-scale data processing. Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing.

Talk #0: Introductions & Meetup Announcements By Chris Fregly, Principal Solution Architect & Antje Barth, Principal Developer Advocates, AI & machine learning @ AWS Talk #1: Amazon DataZone & Data Mesh By Joel Farvault, Principal Solutions Architect @ AWS Data & Analytics Use Amazon DataZone to share, search, & discover data at scale across organizational boundaries. Collaborate on data projects through a unified data analytics portal that gives you a personalized view of all your data while enforcing your governance & compliance policies. Talk #2: Generative AI use cases & deep-dive By Arun Shankar, Sr. Solution Architect @ AWS AI/ML I will provide guidance from how to best showcase our SageMaker Generative AI playground or try-out experience (e.g., prompt engineering guidance), training/ fine tuning capabilities available, selecting instances & real-world deployment considerations in production. We will cover: Guided Generative AI demos, git-hub examples & technical Q&A for public & proprietary models in SageMaker. Differences between various types of learning with these large language models, natural language understanding & generation tasks they can solve & what are the some of the common use cases that are aligned with these tasks.

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This post is co-written by Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, DeepRacer SME at Accenture. The creation of a scalable and hassle-free data science environment is key. It can take a considerable amount of time to launch and configure an environment tailored for a specific use case and even harder to onboard colleagues to collaborate. According to Accenture, companies that manage to efficiently scale AI and ML can achieve nearly triple the return on their investments. Still, not all companies meet their expected returns on their AI/ML journey.

According to a 2020 survey of data scientists conducted by Anaconda, data preparation is one of the critical steps in machine learning (ML) and data analytics workflows, and often very time consuming for data scientists. Data scientists spend about 66% of their time on data preparation and analysis tasks, including loading (19%), cleaning (26%), and visualizing data (21%). Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. With a single click, data scientists and developers can quickly spin up Studio notebooks to explore datasets and build models. If you prefer a GUI-based and interactive interface, you can use Amazon SageMaker Data Wrangler, with over 300 built in visualizations, analyses, and transformations to efficiently process data backed by Spark without writing a single line of code.

As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when they are not. In this post, we explore how companies can improve visibility into their models with centralized dashboards and detailed documentation of their models using two new features: SageMaker Model Cards and the SageMaker Model Dashboard. Both these features are available at no additional charge to SageMaker customers. Model governance is a framework that gives systematic visibility into model development, validation, and usage.

Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues. AWS Lambda is a serverless compute service that can provide real-time ML inference at scale. In this post, we demonstrate a sample data capture feature that can be deployed to a Lambda ML inference workload.

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how well the model fits to the data and what columns are driving the prediction, and allows business analysts to run experiments with varied datasets so they can select the best-performing model. Canvas expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows you to generate accurate ML predictions on your own--without requiring any ML experience or having to write a single line of code. In this post, we showcase how to to train a time series forecasting model faster with quick build training in Canvas.

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