Category: Application Integration

The integration of generative AI agents into business processes is poised to accelerate as organizations recognize the untapped potential of these technologies. Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven architecture and ways of implementing.

In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by HAQM Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using HAQM SageMaker, HAQM EventBridge, AWS Lambda, HAQM Simple Notification Service (HAQM SNS), HashiCorp Terraform, and GitLab CI/CD. The presented MLOps workflow provides a reusable template for managing the ML lifecycle through automation, monitoring, auditability, and scalability, thereby reducing the complexities and costs of maintaining batch inference workloads in production.

This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) […]

As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including […]

In a previous post, we talked about analyzing and tagging assets stored in Veeva Vault PromoMats using HAQM AI services and the Veeva Vault Platform’s APIs. In this post, we explore how to use HAQM AppFlow, a fully managed integration service that enables you to securely transfer data from software as a service (SaaS) applications […]

Having an environment capable of delivering HAQM SageMaker notebook instances quickly allows data scientists and business analysts to efficiently respond to organizational needs. Data is the lifeblood of an organization, and analyzing that data efficiently provides useful insights for businesses. A common issue that organizations encounter is creating an automated pattern that enables development teams […]

This is a guest post written by Gergely Kadi, Senior Systems Engineer and Martin Petraschek-Stummer, Senior Data Engineer at TourRadar. TourRadar is a travel marketplace to connect people to life-enriching travel experiences. When it was launched, TourRadar only offered tours and content in English. As the company grew, we saw an opportunity to expand our […]

Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple programming APIs and custom libraries. Announced at AWS re:Invent 2020, HAQM SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for […]

Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics to determine application performance (the average […]