Remove Google Cloud Remove Machine Learning Remove Scalability
article thumbnail

The AI Future According to Google Cloud Next ’25: My Interesting Finds

Xebia

Google Cloud Next 2025 was a showcase of groundbreaking AI advancements. and the Live API Google continues to push the boundaries of AI with their latest “thinking model” Gemini 2.5. Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. BigFrames 2.0

article thumbnail

Bud Financial helps banks and their customers make more informed decisions using AI with DataStax and Google Cloud

CIO

Embrace scalability One of the most critical lessons from Bud’s journey is the importance of scalability. For Bud, the highly scalable, highly reliable DataStax Astra DB is the backbone, allowing them to process hundreds of thousands of banking transactions a second. Artificial Intelligence, Machine Learning

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Marsh McLennan IT reorg lays foundation for gen AI

CIO

Setting the standard for analytics and AI As the core development platform was refined, Marsh McLennan continued moving workloads to AWS and Azure, as well as Oracle Cloud Infrastructure and Google Cloud Platform. I want to provide an easy and secure outlet that’s genuinely production-ready and scalable.

article thumbnail

Understanding Retrieval-Augmented Generation (RAG) on Google Cloud Platform (GCP)

Xebia

Jeroen will take you along RAG applications, and their implementations on Google Cloud Platform (GCP). Scalability: GCP tools offer a cohesive platform to build, manage, and scale RAG systems. Scalability : Handles large-scale datasets and complex search queries with low latency.

article thumbnail

Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.

article thumbnail

AWS vs. Azure vs. Google Cloud: Comparing Cloud Platforms

Kaseya

In a public cloud, all of the hardware, software, networking and storage infrastructure is owned and managed by the cloud service provider. The public cloud infrastructure is heavily based on virtualization technologies to provide efficient, scalable computing power and storage. Scalability and Elasticity.

article thumbnail

CoreWeave, a GPU-focused cloud compute provider, lands $221M investment

TechCrunch

Fast-forward to today and CoreWeave provides access to over a dozen SKUs of Nvidia GPUs in the cloud, including H100s, A100s, A40s and RTX A6000s, for use cases like AI and machine learning, visual effects and rendering, batch processing and pixel streaming. billion in revenue last year, while Google Cloud and Azure made $75.3