Machine learning (ML) platforms such as Amazon Sagemaker, Azure Machine Learning, and Google Vertex AI are fully managed services that enable data scientists and engineers to easily build, train, and deploy ML models. Common use cases for ML platforms include natural language processing (NLP) models for text analysis and chatbots, personalized recommendation systems for e-commerce web applications and streaming services, and predictive business analytics.

On this episode of Densify Talks, we welcome Gokul Naidu, Senior Manager, Cloud Operations for SAP’s SuccessFactors Product Suite. Andrew and Gokul discuss an array of topics in the episode. The general theme of the discussion focuses on cost management and the importance of being prepared, aware, and executing the right planning in order to avoid sticker shock.

We’re proud to announce the general availability of Power IQ® 9.1, the latest version of Sunbird’s DCIM Monitoring software. This release includes exciting new features including load shift detection, enhanced Raritan and Server Technology bulk firmware update, and residual current monitoring (RCM) Type B.

With the advent of microservices and cloud native, organizations are shifting how they approach software development and deployment to become more agile and respond quickly to continually evolving business needs. These changes result in fundamental transformation for IT.

As AI and machine learning workloads continue to grow in complexity and size, the need for efficient and scalable infrastructure becomes more important than ever. In this tutorial, I will show you how to deploy AI applications on AWS Elastic Kubernetes Service (EKS) with Karpenter from scratch, leveraging GPU resources for high-performance computing.