

Yu-Track Enterprise is an IT platform that allows you to know and manage in real time the behaviour of your workforce. Plan the routes, schedule the visit to your customers and obtain the most important KBI's with which to organize your sales strategy.

Mandiant responds to the world's largest breaches. We combine our frontline expertise and deep understanding of global attacker behavior to respond to breaches and help organizations prepare their defenses and operations against compromise.

Google Cloud Migration Center is a unified migration platform that helps you accelerate your end-to-end cloud migration journey from your current on-premises environment to Google Cloud. With features like cloud spend estimation, asset discovery of your current environment, and a variety of tooling for different migration scenarios, Migration Center provides you with what you need for your migration.

TensorFlow Enterprise Reliability and performance for AI applications with enterprise-grade support and managed services.

A cloud service that manages the wireless communications of devices transmitting in the Citizens Broadband Radio Spectrum (CBRS) band, in order to prevent harmful interference to higher-priority users.

Sole-tenant nodes in Google Compute Engine provide dedicated physical servers exclusively for your project's virtual machines . This setup ensures that your VMs do not share host hardware with VMs from other projects, offering enhanced security and compliance by maintaining physical isolation. Sole-tenant nodes support the same features as standard Compute Engine VMs, including transparent scheduling and block storage, while adding an extra layer of hardware isolation. Key Features and Functionality: - Dedicated Hardware: Each sole-tenant node is a physical server allocated solely to your project, ensuring that only your VMs run on that hardware. - Flexible VM Provisioning: You can provision multiple VMs of various machine types on a single node, optimizing resource utilization. - CPU Overcommit: Over-provision virtual CPU resources by up to two times, allowing for efficient use of host CPUs and cost optimization. - Live Migration: Maintain VM uptime during host maintenance events through live migration capabilities. - Node Affinity Labels: Control VM placement using affinity labels to group or separate workloads based on your requirements. Primary Value and Problem Solved: Sole-tenant nodes address the need for physical isolation in cloud environments, which is crucial for organizations with strict compliance, regulatory, or security requirements. By providing dedicated hardware, they eliminate the "noisy neighbor" effect, ensuring consistent and predictable performance for sensitive workloads. Additionally, they facilitate easier management of software licenses that require per-core or per-processor licensing, as you have full visibility and control over the underlying hardware.

sofia-ml is a suite of fast incremental algorithms for machine learning (sofia-ml) that can be used for training models for classification, regression, ranking, or combined regression and ranking, intended to aid researchers and practitioners who require fast methods for classification and ranking on large, sparse data sets.

Google Cloud VMware Engine Easily lift and shift your VMware-based applications to Google Cloud without changes to your apps, tools, or processes. Includes all the hardware and VMware licenses to run in a dedicated VMware SDDC in Google Cloud.

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.


Organize the world’s information and make it universally accessible and useful.