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Get access to startup experts, your Google Cloud and Firebase costs covered up to $200,000 USD (up to $350,000 USD for AI startups) over 2 years, technical training, business support, and Google-wide offers. To receive benefits, you must have an active Google Cloud account.
TensorFlow Enterprise Reliability and performance for AI applications with enterprise-grade support and managed services.
Enable customers to find and purchase your solutions on multiple cloud providers, direct or through your channel.
Google Cloud's Tau Virtual Machines (VMs are designed to deliver exceptional price-performance for scale-out workloads. Leveraging the latest 3rd Generation AMD EPYC™ processors, Tau VMs offer up to 42% better price-performance compared to general-purpose VMs from other leading cloud providers. This makes them ideal for applications such as web servers, containerized microservices, media transcoding, and large-scale Java applications. Key Features and Functionality: - High Performance: Tau VMs provide up to 60 vCPUs per VM, with 4 GB of memory per vCPU, ensuring robust performance for demanding workloads. - Cost Efficiency: Offering up to 42% higher price-performance, Tau VMs enable significant cost savings for scale-out applications. - Architecture Choice: Customers can choose between x86-based VMs powered by AMD EPYC processors and Arm-based VMs powered by Ampere Altra processors, allowing flexibility to meet specific workload requirements. - Seamless Integration with Google Kubernetes Engine (GKE: Tau VMs are fully supported by GKE, facilitating optimized price-performance for containerized workloads. - Scalability: With predefined VM shapes and up to 32 Gbps networking bandwidth, Tau VMs are well-suited for horizontally scalable applications. Primary Value and User Benefits: Tau VMs address the need for high-performance, cost-effective solutions for scale-out workloads. By offering superior price-performance, they enable businesses to run demanding applications more efficiently, reducing total cost of ownership without compromising on performance. The flexibility in architecture choice and seamless integration with GKE further enhance their appeal for modern, cloud-native applications.
Secret Manager Store API keys, passwords, certificates, and other sensitive data. New customers get $300 in free credits to spend on Secret Manager. All customers get six secret versions for analyzing and storing sensitive data.
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Config Connector is an open source Kubernetes addon that allows you to manage Google Cloud resources through Kubernetes. Many cloud-native development teams work with a mix of configuration systems, APIs, and tools to manage their infrastructure. This mix is often difficult to understand, leading to reduced velocity and expensive mistakes. Config Connector provides a method to configure many Google Cloud services and resources using Kubernetes tooling and APIs.
Google Migrate for Compute Engine is the cloud workload mobility company, that enables enterprises to move even production workloads to the public cloud in minutes, while controlling and automating where data resides.
Gemma 3n is a generative AI model optimized for deployment on everyday devices such as smartphones, laptops, and tablets. It introduces innovations in parameter-efficient processing, including Per-Layer Embedding (PLE) parameter caching and the MatFormer architecture, which collectively reduce computational and memory demands. The model supports audio, text, and visual inputs, enabling a wide range of applications from speech recognition to image analysis. Key Features and Functionality: - Audio Input Handling: Processes sound data for tasks like speech recognition, translation, and audio analysis. - Multimodal Capabilities: Handles visual and text inputs, facilitating comprehensive understanding and analysis of diverse data types. - Vision Encoder: Incorporates a high-performance MobileNet-V5 encoder to enhance the speed and accuracy of visual data processing. - PLE Caching: Utilizes Per-Layer Embedding parameters that can be cached to local storage, reducing memory usage during model execution. - MatFormer Architecture: Employs the Matryoshka Transformer architecture, allowing selective activation of model parameters to decrease computational costs and response times. - Conditional Parameter Loading: Offers the flexibility to load specific parameters dynamically, such as those for vision and audio, optimizing memory usage based on task requirements. - Extensive Language Support: Trained in over 140 languages, enabling broad linguistic capabilities. - 32K Token Context Window: Provides a substantial input context, allowing for the processing of large datasets and complex tasks. Primary Value and User Solutions: Gemma 3n addresses the challenge of deploying advanced AI capabilities on resource-constrained devices by offering a model that balances performance with efficiency. Its parameter-efficient design ensures that users can run sophisticated AI applications without compromising device performance or battery life. The model's support for multiple input modalities—audio, text, and visual—enables developers to create versatile applications that can interpret and generate content across various data types. By providing open weights and licensing for responsible commercial use, Gemma 3n empowers developers to fine-tune and deploy the model in diverse projects, fostering innovation in AI applications across different platforms and devices.
Google Reviews
Verified User in Veterinary
High-Quality Image Results, but usage limits are confusing
Celso P.
Founder & CEO na Demotte Maison | Consultor Polímata | Board Advisory | Escritor | AutorImages not very natural: need to generate results more faithful to the prompt
Farwa A.
Effortlessly Enhances Task Management
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