Best AI tools for< Optimize Machine Performance >

Jungle AI is an AI application that offers solutions to improve machine performance and uptime across various industries such as wind, solar, manufacturing, and maritime. By leveraging AI technology, Jungle AI provides real-time insights into asset performance, increases production efficiency, and prevents unplanned downtime. The application is trusted by global teams and has a proven track record of delivering results through advanced AI algorithms and predictive analytics.

ONNX Runtime is a production-grade AI engine designed to accelerate machine learning training and inferencing in various technology stacks. It supports multiple languages and platforms, optimizing performance for CPU, GPU, and NPU hardware. ONNX Runtime powers AI in Microsoft products and is widely used in cloud, edge, web, and mobile applications. It also enables large model training and on-device training, offering state-of-the-art models for tasks like image synthesis and text generation.

Keebo is an AI tool designed for Snowflake optimization, offering automated query, cost, and tuning optimization. It is the only fully-automated Snowflake optimizer that dynamically adjusts to save customers 25% and more. Keebo's patented technology, based on cutting-edge research, optimizes warehouse size, clustering, and memory without impacting performance. It learns and adjusts to workload changes in real-time, setting up in just 30 minutes and delivering savings within 24 hours. The tool uses telemetry metadata for optimizations, providing full visibility and adjustability for complex scenarios and schedules.

Cirrascale Cloud Services is an AI tool that offers cloud solutions for Artificial Intelligence applications. The platform provides a range of cloud services and products tailored for AI innovation, including NVIDIA GPU Cloud, AMD Instinct Series Cloud, Qualcomm Cloud, Graphcore, Cerebras, and SambaNova. Cirrascale's AI Innovation Cloud enables users to test and deploy on leading AI accelerators in one cloud, democratizing AI by delivering high-performance AI compute and scalable deep learning solutions. The platform also offers professional and managed services, tailored multi-GPU server options, and high-throughput storage and networking solutions to accelerate development, training, and inference workloads.

Vindey is an industry-leading AI-powered CRM platform designed for property management and sales solutions. It offers real-time smart suggestions, automation features, and intelligent sales automation to transform customer experiences. Vindey handles the entire sales pipeline, from lead qualification to deal closing, while providing personalized interactions and seamless lead management. The platform also offers AI-powered payment insights, workflow automation, and tailored solutions for various industries, such as sales, healthcare, real estate, education, and manufacturing.

Perspect is an AI-powered platform designed for high-performance software teams. It offers real-time insights into team contributions and impact, optimizing developer experience, and rewarding high-performers. With 50+ integrations, Perspect enables visualization of impact, benchmarking performance, and uses machine learning models to identify and eliminate blockers. The platform is deeply integrated with web3 wallets and offers built-in reward mechanisms. Managers can align resources around crucial KPIs, identify top talent, and prevent burnout. Perspect aims to enhance team productivity and employee retention through AI and ML technologies.

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

Weavel is an AI tool designed to revolutionize prompt engineering for large language models (LLMs). It offers features such as tracing, dataset curation, batch testing, and evaluations to enhance the performance of LLM applications. Weavel enables users to continuously optimize prompts using real-world data, prevent performance regression with CI/CD integration, and engage in human-in-the-loop interactions for scoring and feedback. Ape, the AI prompt engineer, outperforms competitors on benchmark tests and ensures seamless integration and continuous improvement specific to each user's use case. With Weavel, users can effortlessly evaluate LLM applications without the need for pre-existing datasets, streamlining the assessment process and enhancing overall performance.

The Intel Gaudi AI accelerator developer website provides resources, guidance, tools, and support for building, migrating, and optimizing AI models. It offers software, model references, libraries, containers, and tools for training and deploying Generative AI and Large Language Models. The site focuses on the Intel Gaudi accelerators, including tutorials, documentation, and support for developers to enhance AI model performance.

OtterTune was a database tuning service start-up founded by Carnegie Mellon University. Unfortunately, the company is no longer operational. The founder, DJ OT, is currently in prison for a parole violation. Despite the challenges, the team and investors were dedicated to the project. OtterTune aimed to optimize database performance through innovative tuning techniques.

AdCopy is an AI-powered advertising platform that helps businesses create high-quality ads and optimize their ad campaigns. The platform uses AI to generate ad copy, create ad creatives, and provide insights into ad performance. AdCopy is designed to help businesses save time and money on their advertising campaigns, while also improving their results.

Valohai is a scalable MLOps platform that enables Continuous Integration/Continuous Deployment (CI/CD) for machine learning and pipeline automation on-premises and across various cloud environments. It helps streamline complex machine learning workflows by offering framework-agnostic ML capabilities, automatic versioning with complete lineage of ML experiments, hybrid and multi-cloud support, scalability and performance optimization, streamlined collaboration among data scientists, IT, and business units, and smart orchestration of ML workloads on any infrastructure. Valohai also provides a knowledge repository for storing and sharing the entire model lifecycle, facilitating cross-functional collaboration, and allowing developers to build with total freedom using any libraries or frameworks.

EnterpriseAI is an advanced computing platform that focuses on the intersection of high-performance computing (HPC) and artificial intelligence (AI). The platform provides in-depth coverage of the latest developments, trends, and innovations in the AI-enabled computing landscape. EnterpriseAI offers insights into various sectors such as financial services, government, healthcare, life sciences, energy, manufacturing, retail, and academia. The platform covers a wide range of topics including AI applications, security, data storage, networking, and edge/IoT technologies.

Peoplelogic is a suite of AI-enabled people and performance tools that helps businesses grow their people, cultivate better leaders, and increase business efficiency. The platform includes tools for driving strategic alignment, making data-driven people decisions, and building a culture of high performance and trust. Peoplelogic is used by over 1,200 organizations to improve employee engagement, reduce burnout, and optimize their people, process, and technology.

Custobots is an AI-powered application that revolutionizes digital commerce by acting as autonomous agents for human consumers or businesses in online marketplaces. These intelligent software entities make purchasing decisions, negotiate prices, and complete transactions on behalf of users. Custobots possess advanced decision-making capabilities, analyze market conditions, compare prices, negotiate with sellers, and adapt from past experiences to improve future performance. They offer a cutting-edge approach to e-commerce, leveraging technologies like Natural Language Processing, Machine Learning, Blockchain, and API integrations to navigate online marketplaces autonomously.

Portkey is a control panel for production AI applications that offers an AI Gateway, Prompts, Guardrails, and Observability Suite. It enables teams to ship reliable, cost-efficient, and fast apps by providing tools for prompt engineering, enforcing reliable LLM behavior, integrating with major agent frameworks, and building AI agents with access to real-world tools. Portkey also offers seamless AI integrations for smarter decisions, with features like managed hosting, smart caching, and edge compute layers to optimize app performance.

CreatorML is an AI-powered platform designed to help YouTube creators optimize their content and grow their channels. Using machine learning, CreatorML's tools can predict how well a video will perform before it's even published, suggest title and thumbnail ideas, and provide insights into what's trending on YouTube. CreatorML is designed for YouTube creators of all levels, from beginners to experienced professionals. It offers a variety of subscription plans to fit every budget and need.

FuriosaAI is an AI application that offers Hardware RNGD for LLM and Multimodality, as well as WARBOY for Computer Vision. It provides a comprehensive developer experience through the Furiosa SDK, Model Zoo, and Dev Support. The application focuses on efficient AI inference, high-performance LLM and multimodal deployment capabilities, and sustainable mass adoption of AI. FuriosaAI features the Tensor Contraction Processor architecture, software for streamlined LLM deployment, and a robust ecosystem support. It aims to deliver powerful and efficient deep learning acceleration while ensuring future-proof programmability and efficiency.

FineTuneAIs.com is a platform that specializes in custom AI model fine-tuning. Users can fine-tune their AI models to achieve better performance and accuracy. The platform requires JavaScript to be enabled for optimal functionality.

Adrsta Marketing Science Platform is an AI-powered platform that offers integrated media planning, buying, and measurement solutions to drive advertising performance. It utilizes machine learning algorithms to optimize advertising strategies and provide comprehensive insights for advertisers, agencies, platforms, and publishers. Adrsta's Marketing Mix Models, Bid Optimizer, and Synthetic Control Lift features enable users to plan, buy, and measure advertising campaigns efficiently, leading to improved ROI and campaign performance.

ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. Key value propositions of ExecuTorch are: * **Portability:** Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers. * **Productivity:** Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms. * **Performance:** Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

Llama Coder is a self-hosted Github Copilot replacement for VS Code that provides autocomplete using Ollama and Codellama. It works best with Mac M1/M2/M3 or RTX 4090, offering features like fast performance, no telemetry or tracking, and compatibility with any coding language. Users can install Ollama locally or on a dedicated machine for remote usage. The tool supports different models like stable-code and codellama with varying RAM/VRAM requirements, allowing users to optimize performance based on their hardware. Troubleshooting tips and a changelog are also provided for user convenience.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

Data Prep Kit is a community project aimed at democratizing and speeding up unstructured data preparation for LLM app developers. It provides high-level APIs and modules for transforming data (code, language, speech, visual) to optimize LLM performance across different use cases. The toolkit supports Python, Ray, Spark, and Kubeflow Pipelines runtimes, offering scalability from laptop to datacenter-scale processing. Developers can contribute new custom modules and leverage the data processing library for building data pipelines. Automation features include workflow automation with Kubeflow Pipelines for transform execution.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

The Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring Machine Learning and Large Language models in production. It helps proactively identify and address potential performance issues by analyzing data quality, model quality, and model drift. The repository contains files and projects for running the platform, including UI, API, SDK, and Spark components. Installation using Docker compose is provided, allowing deployment with a K3s cluster and interaction with a k9s container. The platform documentation includes a step-by-step guide for installation and creating dashboards. Community engagement is encouraged through a Discord server. The roadmap includes adding functionalities for batch and real-time workloads, covering various model types and tasks.

Awesome-AI-SEO is a curated list of powerful AI tools and platforms designed to transform your SEO strategy. This repository gathers the most effective tools that leverage machine learning and artificial intelligence to automate and enhance key aspects of search engine optimization. Whether you are an SEO professional, digital marketer, or website owner, these tools can help you optimize your site, improve your search rankings, and increase organic traffic with greater precision and efficiency. The list features AI tools covering on-page and off-page optimization, competitor analysis, rank tracking, and advanced SEO analytics. By utilizing cutting-edge technologies, businesses can stay ahead of the competition by uncovering hidden keyword opportunities, optimizing content for better visibility, and automating time-consuming SEO tasks. With frequent updates, Awesome-AI-SEO is your go-to resource for discovering the latest AI-driven innovations in the SEO space.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

Vodalus Expert LLM Forge is a tool designed for crafting datasets and efficiently fine-tuning models using free open-source tools. It includes components for data generation, LLM interaction, RAG engine integration, model training, fine-tuning, and quantization. The tool is suitable for users at all levels and is accompanied by comprehensive documentation. Users can generate synthetic data, interact with LLMs, train models, and optimize performance for local execution. The tool provides detailed guides and instructions for setup, usage, and customization.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

Numpy.NET is the most complete .NET binding for NumPy, empowering .NET developers with extensive functionality for scientific computing, machine learning, and AI. It provides multi-dimensional arrays, matrices, linear algebra, FFT, and more via a strong typed API. Numpy.NET does not require a local Python installation, as it uses Python.Included to package embedded Python 3.7. Multi-threading must be handled carefully to avoid deadlocks or access violation exceptions. Performance considerations include overhead when calling NumPy from C# and the efficiency of data transfer between C# and Python. Numpy.NET aims to match the completeness of the original NumPy library and is generated using CodeMinion by parsing the NumPy documentation. The project is MIT licensed and supported by JetBrains.

Expert en optimisation de performance et traitement de données

我分析并优化代码以提高性能和可读性。

AI 360 Solutions. Designed to provide all-encompassing solutions in the field of artificial intelligence.

Assistant Python ultra-personnalisé, conçu pour transformer les programmeurs de tous niveaux en maîtres de Python. Spécialisé dans l'analyse approfondie du code, les tutoriels interactifs, et l'optimisation de performance.

优化代码，提升运行速度的专家

Maximize your Stock Portfolio Performance with the Pareto Principle

Guides cloud strategy and architecture to optimize business operations.

Badminton Performance Analyst

Optimizes cloud-based networks for efficient organizational operations.

I'm Back Propagation, here to help you understand and apply back propagation techniques to your AI models.

An expert MLOps engineer assisting in DevOps and pipeline optimization.

A Python programming guide and code analyst

I'm a Python and PyTorch expert with knowledge of ML infrastructure requirements ready to help you build and scale your ML projects.

AI programming assistant specializing in clear, efficient coding

Checks all open AI systems for downtime.

Automated AI experiment tool for evaluating prompt strategies.

AI-powered sports analytics expert for sportsmind.io users.

中文代码编译专家

AI radar design tool with comprehensive proposals