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
This blog post is co-written with Heidi Vogel Brockmann and Ronald Brockmann from GuardianGamer. Millions of families face a common challenge: how to keep children safe in online gaming without sacrificing the joy and social connection these games provide. In this post, we share how GuardianGamer—a member of the AWSContinue Reading
This post was co-authored with Johann Wildgruber, Dr. Jens Kohl, Thilo Bindel, and Luisa-Sophie Gloger from BMW Group. The BMW Group—headquartered in Munich, Germany—is a vehicle manufacturer with more than 154,000 employees, and 30 production and assembly facilities worldwide as well as research and development locations across 17 countries. Today, theContinue Reading
This blog post is co-written with Gene Arnold from Alation. To build a generative AI-based conversational application integrated with relevant data sources, an enterprise needs to invest time, money, and people. First, you would need build connectors to the data sources. Next you need to index this data to makeContinue Reading
Cloud technologies are progressing at a rapid pace. Businesses are adopting new innovations and technologies to create cutting-edge solutions for their customers. However, security is a big risk when adopting the latest technologies. Enterprises often rely on reactive security monitoring and notification techniques, but those techniques might not be sufficientContinue Reading
This post is co-written with Steven Craig from Hearst. To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. However, Cloud Center of Excellence (CCoE) teams often can be perceived as bottlenecks to organizational transformation due to limited resources and overwhelmingContinue Reading
In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as GoogleContinue Reading
Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Conventional ML development cycles take weeks to many months and requires sparseContinue Reading
Modern organizations increasingly depend on robust cloud infrastructure to provide business continuity and operational efficiency. Operational health events – including operational issues, software lifecycle notifications, and more – serve as critical inputs to cloud operations management. Inefficiencies in handling these events can lead to unplanned downtime, unnecessary costs, and revenueContinue Reading
Incorporating generative artificial intelligence (AI) into your development lifecycle can offer several benefits. For example, using an AI-based coding companion such as Amazon Q Developer can boost development productivity by up to 30 percent. Additionally, reducing the developer context switching that stems from frequent interactions with many different development toolsContinue Reading
