Welcome to above the clouds
GCP – Go beyond data: Four steps to master enterprise excellence
Editor’s note: This is the first in a series of five blog posts dedicated to data transformation powered by Google Cloud and its ecosystem of data and analytics partners. Everyone is trying to determine how best to leverage new AI technologies. But to be able to get the most out of AI, you need a […]
GCP – How to integrate your Cloud SQL for MySQL database with Vertex AI & vector search
Search is a critical component of many modern applications – whether searching for products in an online storefront, finding solutions to your customers’ support cases, or building the perfect playlist. But traditional keyword searches often miss the deeper meaning of data. Vector embeddings, however, capture the complexities of your data, enabling highly accurate and powerful […]
GCP – How Renault Group is using Google’s software-defined vehicle industry solution
It’s funny to think of Renault Group, the massive European car manufacturer, as a software company, but in many ways, it is. Renault Group subsidiary Ampere Software Technology is dedicated to developing and integrating advanced software solutions for intelligent electric vehicles, aiming to create software-defined vehicles (SDVs) with enhanced customer experiences and new services. Ampere […]
GCP – Implementing High-Performance LLM Serving on GKE: An Inference Gateway Walkthrough
The excitement around open Large Language Models like Gemma, Llama, Mistral, and Qwen is evident, but developers quickly hit a wall. How do you deploy them effectively at scale? Traditional load balancing algorithms fall short, as they fail to account for GPU/TPU load status, leading to inefficient routing for computationally intensive AI inference with its […]
AWS – Amazon SageMaker simplifies data management with automated lakehouse onboarding and metadata ingestion
Today, Amazon SageMaker launches two new capabilities designed to simplify data management and governance. The first capability, automated lakehouse onboarding, allows customers to automatically ingest metadata for datasets, such as Glue Data Catalog tables, into SageMaker Catalog. This occurs when creating a new SageMaker Unified Studio domain or by updating existing domains. It removes the […]
AWS – Amazon SageMaker Catalog adds support for Amazon S3 general purpose buckets
You can now add Amazon S3 general purpose buckets to the Amazon SageMaker Catalog. This helps data scientists, engineers, and business analysts to easily discover and access datasets in Amazon S3, while giving data producers the ability to maintain granular security controls. As a result, teams get efficient access to the right datasets in S3, […]
AWS – Amazon SageMaker introduces a visual workflows builder
Amazon SageMaker now offers a visual builder experience for creating and managing workflows. This feature is part of the next generation of Amazon SageMaker – the center for all your data, analytics, and AI, and is available within SageMaker Unified Studio, a single data and AI development environment. Visual workflows in Amazon SageMaker provides a […]
AWS – Announcing Amazon S3 Vectors (Preview)—First cloud object storage with native support for storing and querying vectors
Amazon S3 Vectors delivers purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. By reducing the cost of uploading, storing, and querying vectors by up to 90%, S3 Vectors makes it cost-effective to create and use large vector datasets to improve the memory and context […]
AWS – Amazon Redshift announces support for automatic refresh of materialized views on Apache Iceberg tables
Amazon Redshift now supports automatic refresh of materialized views that are defined on external Apache Iceberg tables in the Amazon S3 data lake. With this update, Amazon Redshift will automatically refresh a materialized view defined on Apache Iceberg tables, that reside in the Amazon S3 bucket of the Amazon data lake, when there is new […]
AWS – TwelveLabs models now available fully managed in Amazon Bedrock
TwelveLabs’ Marengo 2.7 and Pegasus 1.2 multimodal foundation models are now available in Amazon Bedrock. Marengo 2.7 is a video embedding model proficient at performing tasks such as search and classification, enabling enhanced video understanding, while Pegasus 1.2 is a video language model that can generate text based on your video data. By integrating these […]