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Mitti (by SafetyCulture)Mitti (by SafetyCulture) is an operations system designed for frontline teams that need one connected platform for people, workflows, standards, and operational data. The platform helps organizations understand what is happening across every site and shift, identify what needs attention, and help workers perform at their best. Mitti supports quality management, compliance, asset maintenance, training, onboarding, inspections, issue reporting, task management, communications, document management, analytics, contractor management, investigations, sensors, IoT, lone worker safety, and insurance workflows. Its inspection tools turn checklists into connected workflows, while task management helps teams fix problems and close them out. AI Assistant helps users create checklists, training, and operational content, and AI Issue Capture converts photos or voice notes into actionable issues. Training tools help teams build on-the-job learning, while communications features send messages across the workforce and confirm they have been read. Asset maintenance features help track assets in real time, monitor maintenance needs, and reduce breakdowns. Analytics, benchmarking, investigations, and integrations help leaders see performance across the operation, identify root causes, connect to existing tools, and continuously improve. Mitti is built for industries such as manufacturing, facilities management, hospitality, construction, and retail, where distributed frontline teams need simple tools that fit how work actually happens. The platform is designed to digitize paper-based processes, reduce stock loss, save time, improve response rates, support compliance, and keep workers safer. By combining inspections, training, issue capture, asset maintenance, operational AI, analytics, communications, documents, tasks, and frontline workflow management, Mitti helps organizations turn everyday signals into measurable outcomes.
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QlooQloo, known as the "Cultural AI," excels in interpreting and predicting global consumer preferences. This privacy-centric API offers insights into worldwide consumer trends, boasting a catalog of hundreds of millions of cultural entities. By leveraging a profound understanding of consumer behavior, our API delivers personalized insights and contextualized recommendations. We tap into a diverse dataset encompassing over 575 million individuals, locations, and objects. Our innovative technology enables users to look beyond mere trends, uncovering the intricate connections that shape individual tastes in their cultural environments. The extensive library includes a wide array of entities, such as brands, music, film, fashion, and notable figures. Results are generated in mere milliseconds and can be adjusted based on factors like regional influences and current popularity. This service is ideal for companies aiming to elevate their customer experience with superior data. Additionally, our premier recommendation API tailors results by analyzing demographics, preferences, cultural entities, geolocation, and relevant metadata to ensure accuracy and relevance.
What is vLLM?
vLLM is an innovative library specifically designed for the efficient inference and deployment of Large Language Models (LLMs). Originally developed at UC Berkeley's Sky Computing Lab, it has evolved into a collaborative project that benefits from input by both academia and industry. The library stands out for its remarkable serving throughput, achieved through its unique PagedAttention mechanism, which adeptly manages attention key and value memory. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, leveraging technologies such as FlashAttention and FlashInfer to enhance model execution speed significantly. In addition, vLLM accommodates several quantization techniques, including GPTQ, AWQ, INT4, INT8, and FP8, while also featuring speculative decoding capabilities. Users can effortlessly integrate vLLM with popular models from Hugging Face and take advantage of a diverse array of decoding algorithms, including parallel sampling and beam search. It is also engineered to work seamlessly across various hardware platforms, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, which assures developers of its flexibility and accessibility. This extensive hardware compatibility solidifies vLLM as a robust option for anyone aiming to implement LLMs efficiently in a variety of settings, further enhancing its appeal and usability in the field of machine learning.
What is DeePhi Quantization Tool?
This cutting-edge tool is crafted for the quantization of convolutional neural networks (CNNs), enabling the conversion of weights, biases, and activations from 32-bit floating-point (FP32) to 8-bit integer (INT8) format, as well as other bit depths. By utilizing this tool, users can significantly boost inference performance and efficiency while maintaining high accuracy. It supports a variety of common neural network layer types, including convolution, pooling, fully-connected layers, and batch normalization, among others. Notably, the quantization procedure does not necessitate retraining the network or the use of labeled datasets; a single batch of images suffices for the process. Depending on the size of the neural network, this quantization can be achieved in just seconds or extend to several minutes, allowing for rapid model updates. Additionally, the tool is specifically designed to work seamlessly with DeePhi DPU, generating the necessary INT8 format model files for DNNC integration. By simplifying the quantization process, this tool empowers developers to create models that are not only efficient but also resilient across different applications. Ultimately, it represents a significant advancement in optimizing neural networks for real-world deployment.
Integrations Supported
Database Mart
Docker
Hugging Face
KServe
Kubernetes
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Thunder Compute
API Availability
Has API
API Availability
Pricing Information
Pricing not provided
Pricing Information
$0.90 per hour
Supported Platforms
SaaS
Supported Platforms
SaaS
Customer Service / Support
24 Hour Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Training Options
Documentation Hub
Company Facts
Organization Name
vLLM
Company Location
United States
Company Website
vllm.ai
Company Facts
Organization Name
DeePhi Quantization Tool
Company Website
aws.amazon.com/marketplace/pp/prodview-bwtx6kzwg3gva
Categories and Features
AI Inference
Not specified
Categories and Features
AI Inference
Not specified
Neural Network
Not specified