Best AI tools for< Check Deployment Status >

The website is a platform that provides error validation services for endpoints. Users can verify their endpoint URLs and check the status of their deployments. It helps in identifying issues related to endpoint existence and completion of deployments. The platform aims to ensure the smooth functioning of endpoints by detecting errors and providing relevant feedback to users.

The website page seems to be a placeholder or error page with the message 'Site Not Found'. It indicates that the user may not have deployed an app yet or may have an empty directory. The page suggests referring to hosting documentation to deploy the first app. The site appears to be under construction or experiencing technical issues.

MacWhisper is a native macOS application that utilizes OpenAI's Whisper technology for transcribing audio files into text. It offers a user-friendly interface for recording, transcribing, and editing audio, making it suitable for various use cases such as transcribing meetings, lectures, interviews, and podcasts. The application is designed to protect user privacy by performing all transcriptions locally on the device, ensuring that no data leaves the user's machine.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::l44g5-1727283130745-f99c9f7f28f4) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::22md2-1720772812453-4893618e160a) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::n894q-1726678978147-1c9e4ad82a70) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::qhrjt-1726765433586-bc18f7adaa0c) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::vphzj-1726938947015-5b9ee22b5622) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ggptb-1727542270172-dbd5ec692f5f) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::gwh5l-1728060486264-1caee7008fee) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::k52hk-1728147113444-17b9d56f17fd) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::7crbp-1720289011850-d12041b250e9) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code and an ID for reference, along with a suggestion to check the documentation for more information and troubleshooting.

The website displays a 404 error message indicating that the requested deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ltfvp-1727369324219-e2d8330c3f8d) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::7rd4m-1725901316906-8c71a7a2cbd7) for reference. Users are directed to check the documentation for further information and troubleshooting.

BluePond GenAI PaaS is an automation and insights powerhouse tailored for Property and Casualty Insurance. It offers end-to-end execution support from GenAI data scientists, engineers & human-in-the-loop processing. The platform provides automated intake extraction, classification enrichment, validation, complex document analysis, workflow automation, and decisioning. Users benefit from rapid deployment, complete control of data & IP, and pre-trained P&C domain library. BluePond GenAI PaaS aims to energize and expedite GenAI initiatives throughout the insurance value chain.

Diffblue Cover is an autonomous AI-powered unit test writing tool for Java development teams. It uses next-generation autonomous AI to automate unit testing, freeing up developers to focus on more creative work. Diffblue Cover can write a complete and correct Java unit test every 2 seconds, and it is directly integrated into CI pipelines, unlike AI-powered code suggestions that require developers to check the code for bugs. Diffblue Cover is trusted by the world's leading organizations, including Goldman Sachs, and has been proven to improve quality, lower developer effort, help with code understanding, reduce risk, and increase deployment frequency.

Tangram Vision is a company that provides sensor calibration tools and infrastructure for robotics and autonomous vehicles. Their products include MetriCal, a high-speed bundle adjustment software for precise sensor calibration, and AutoCal, an on-device, real-time calibration health check and adjustment tool. Tangram Vision also offers a high-resolution depth sensor called HiFi, which combines high-resolution depth data with high-powered AI capabilities. The company's mission is to accelerate the development and deployment of autonomous systems by providing the tools and infrastructure needed to ensure the accuracy and reliability of sensors.

StreamDeploy is an AI-powered cloud deployment platform designed to streamline and secure application deployment for agile teams. It offers a range of features to help developers maximize productivity and minimize costs, including a Dockerfile generator, automated security checks, and support for continuous integration and delivery (CI/CD) pipelines. StreamDeploy is currently in closed beta, but interested users can book a demo or follow the company on Twitter for updates.

Essay Check is a free AI-powered tool that helps students, teachers, content creators, SEO specialists, and legal experts refine their writing, detect plagiarism, and identify AI-generated content. With its user-friendly interface and advanced algorithms, Essay Check analyzes text to identify grammatical errors, spelling mistakes, instances of plagiarism, and the likelihood that content was written using AI. The tool provides detailed feedback and suggestions to help users improve their writing and ensure its originality and authenticity.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.

Pro React Admin is a comprehensive React admin template that includes features such as theme switching, custom component theming, nested routing, webpack optimization, TypeScript support, multi-tabs, internationalization, code styling, commit message configuration, error handling, code splitting, component documentation generation, and more. It also provides tools for mock server implementation, deployment, linting, formatting, and continuous code review. The template supports various technologies like React, React Router, Webpack, Babel, Ant Design, TypeScript, and Vite, making it suitable for building efficient and scalable React admin applications.

Azure HPC On-Demand Platform (az-hop) provides an end-to-end deployment mechanism for a base HPC infrastructure on Azure. It delivers a complete HPC cluster solution ready for users to run applications, which is easy to deploy and manage for HPC administrators. az-hop leverages various Azure building blocks and can be used as-is or easily customized and extended to meet any uncovered requirements. Industry-standard tools like Terraform, Ansible, and Packer are used to provision and configure this environment, which contains: - An HPC OnDemand Portal for all user access, remote shell access, remote visualization access, job submission, file access, and more - An Active Directory for user authentication and domain control - Open PBS or SLURM as a Job Scheduler - Dynamic resources provisioning and autoscaling is done by Azure CycleCloud pre-configured job queues and integrated health-checks to quickly avoid non-optimal nodes - A Jumpbox to provide admin access - A common shared file system for home directory and applications is delivered by Azure Netapp Files - Grafana dashboards to monitor your cluster - Remote Visualization with noVNC and GPU acceleration with VirtualGL

ClearML Serving is a command line utility for model deployment and orchestration, enabling model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based solution. It supports machine learning models like Scikit Learn, XGBoost, LightGBM, and deep learning models like TensorFlow, PyTorch, ONNX. It provides a customizable RestAPI for serving, online model deployment, scalable solutions, multi-model per container, automatic deployment, canary A/B deployment, model monitoring, usage metric reporting, metric dashboard, and model performance metrics. ClearML Serving is modular, scalable, flexible, customizable, and open source.

Vidur is a high-fidelity and extensible LLM inference simulator designed for capacity planning, deployment configuration optimization, testing new research ideas, and studying system performance of models under different workloads and configurations. It supports various models and devices, offers chrome trace exports, and can be set up using mamba, venv, or conda. Users can run the simulator with various parameters and monitor metrics using wandb. Contributions are welcome, subject to a Contributor License Agreement and adherence to the Microsoft Open Source Code of Conduct.

Eidolon is an open-source agent services framework that helps developers design and deploy agent-based services. It simplifies agent deployment, facilitates agent-to-agent communication, and enables painless component customization and upgrades. Eidolon's modular architecture allows developers to easily swap out components, such as language models, reinforcement learning implementations, tools, and more. This flexibility minimizes vendor lock-in and reduces the effort required to upgrade agent components. As the AI landscape rapidly evolves, Eidolon empowers developers to adapt their agents to meet changing requirements.

Eden AI aims to simplify the use and deployment of AI technologies by providing a unique API that connects to all the best AI engines. With the rise of **AI as a Service** , a lot of companies provide off-the-shelf trained models that you can access directly through an API. These companies are either the tech giants (Google, Microsoft , Amazon) or other smaller, more specialized companies, and there are hundreds of them. Some of the most known are : DeepL (translation), OpenAI (text and image analysis), AssemblyAI (speech analysis). There are **hundreds of companies** doing that. We're regrouping the best ones **in one place** !

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

BitBLAS is a library for mixed-precision BLAS operations on GPUs, for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$. BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in GPTQ, the $W_{INT2}A_{FP16}$ in BitDistiller, the $W_{INT2}A_{INT8}$ in BitNet-b1.58. BitBLAS is based on techniques from our accepted submission at OSDI'24.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

Rill Flow is a high-performance, scalable distributed workflow orchestration service that supports the execution of tens of millions of tasks per day with task execution latency less than 100ms. It is distributed and supports the orchestration and scheduling of heterogeneous distributed systems. Rill Flow is easy to use, supporting visual process orchestration and plug-in access. It is cloud native, allowing for cloud native container deployment and cloud native function orchestration. Additionally, Rill Flow supports rapid integration of LLM model services.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.

The Azure Open AI Application Landing Zone Solution Accelerator aims to assist in setting up development and production environments for Generative AI solutions using Azure Open AI and Azure Services. It provides deployment templates for common Gen AI solution patterns and offers customization options. The solution accelerator also offers best practices for technology usage in various scenarios.

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. It is developed under the MIT License and powers inference behind Gradient.ai. The API allows users to deploy models from SentenceTransformers, offers fast inference backends utilizing various accelerators, dynamic batching for efficient processing, correct and tested implementation, and easy-to-use API built on FastAPI with Swagger documentation. Users can embed text, rerank documents, and perform text classification tasks using the tool. Infinity supports various models from Huggingface and provides flexibility in deployment via CLI, Docker, Python API, and cloud services like dstack. The tool is suitable for tasks like embedding, reranking, and text classification.

Hezar is an all-in-one AI library designed specifically for the Persian community. It brings together various AI models and tools, making it easy to use AI with just a few lines of code. The library seamlessly integrates with Hugging Face Hub, offering a developer-friendly interface and task-based model interface. In addition to models, Hezar provides tools like word embeddings, tokenizers, feature extractors, and more. It also includes supplementary ML tools for deployment, benchmarking, and optimization.

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

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I check code samples for common mistakes and accuracy

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I'll help you sense-check and define Behavioral Insights for your ideas!

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ChatGPT Essay Writer helps you to write essays with OpenAI. Generate Professional Essays with Plagiarism Check, Formatting, Cost Estimation & More.

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Ai PDF is a GPT (uses the popular Ai PDF plugin) that allows you to chat and ask questions of your PDF documents and have it explained to you by ChatGPT. We also include page references to help you fact-check all answers.