Best AI tools for< Scale Resources >

Heroku is a cloud platform as a service (PaaS) that enables developers to build, run, and operate applications entirely in the cloud. It supports several programming languages and offers a range of tools and services to streamline the development process. With Heroku, developers can focus on building their applications without worrying about infrastructure management.

Substratus.AI is a fully managed private LLMs platform that allows users to serve LLMs (Llama and Mistral) in their own cloud account. It enables users to keep control of their data while reducing OpenAI costs by up to 10x. With Substratus.AI, users can utilize LLMs in production in hours instead of weeks, making it a convenient and efficient solution for AI model deployment.

KubeHelper is an AI-powered tool designed to reduce Kubernetes downtime by providing troubleshooting solutions and command searches. It seamlessly integrates with Slack, allowing users to interact with their Kubernetes cluster in plain English without the need to remember complex commands. With features like troubleshooting steps, command search, infrastructure management, scaling capabilities, and service disruption detection, KubeHelper aims to simplify Kubernetes operations and enhance system reliability.

The website is a platform that provides web server hosting services. It helps users set up and manage their web servers efficiently. Users can easily deploy their websites and applications on the server, ensuring a seamless online presence. The platform offers a user-friendly interface and reliable hosting solutions to meet various needs.

FluidStack is a leading GPU cloud platform designed for AI and LLM (Large Language Model) training. It offers unlimited scale for AI training and inference, allowing users to access thousands of fully-interconnected GPUs on demand. Trusted by top AI startups, FluidStack aggregates GPU capacity from data centers worldwide, providing access to over 50,000 GPUs for accelerating training and inference. With 1000+ data centers across 50+ countries, FluidStack ensures reliable and efficient GPU cloud services at competitive prices.

LAION is a non-profit organization that provides datasets, tools, and models to advance machine learning research. The organization's goal is to promote open public education and encourage the reuse of existing datasets and models to reduce the environmental impact of machine learning research.

SaasPedia is a website that provides resources and services to help SaaS businesses grow and scale their traffic, sales, and revenue with organic marketing campaigns and strategies. The website offers a variety of resources, including a blog, ebooks, webinars, and tools, as well as services such as cold outreach, SEO, and content marketing. SaasPedia also has a directory of SaaS companies and a list of SaaS-related podcasts and YouTube channels.

Cohere is a customer support platform that uses AI to help businesses resolve tickets faster and reduce costs. It offers a range of features, including automated ticket resolution, personalized answers, step-by-step guidance, and advanced analytics. Cohere integrates with existing support resources and can be implemented quickly and easily. It has been used by leading companies to achieve industry-leading outcomes, including Ramp, Loom, Rippling, OpenPhone, Flock Safety, and Podium.

SolidGrids is an AI-powered image enhancement tool designed specifically for e-commerce businesses. It automates the image post-production process, saving time and resources. With SolidGrids, you can easily remove backgrounds, enhance product images, and create consistent branding across your e-commerce site. The platform offers seamless cloud integrations and is cost-effective compared to traditional methods.

The Center for AI Safety (CAIS) is a research and field-building nonprofit organization based in San Francisco. They conduct impactful research, advocacy projects, and provide resources to reduce societal-scale risks associated with artificial intelligence (AI). CAIS focuses on technical AI safety research, field-building projects, and offers a compute cluster for AI/ML safety projects. They aim to develop and use AI safely to benefit society, addressing inherent risks and advocating for safety standards.

The Center for AI Safety (CAIS) is a research and field-building nonprofit based in San Francisco. Their mission is to reduce societal-scale risks associated with artificial intelligence (AI) by conducting impactful research, building the field of AI safety researchers, and advocating for safety standards. They offer resources such as a compute cluster for AI/ML safety projects, a blog with in-depth examinations of AI safety topics, and a newsletter providing updates on AI safety developments. CAIS focuses on technical and conceptual research to address the risks posed by advanced AI systems.

Motific.ai is a responsible GenAI tool powered by data at scale. It offers a fully managed service with natural language compliance and security guardrails, an intelligence service, and an enterprise data-powered, end-to-end retrieval augmented generation (RAG) service. Users can rapidly deliver trustworthy GenAI assistants and API endpoints, configure assistants with organization's data, optimize performance, and connect with top GenAI model providers. Motific.ai enables users to create custom knowledge bases, connect to various data sources, and ensure responsible AI practices. It supports English language only and offers insights on usage, time savings, and model optimization.

JobSynergy is an AI-powered platform that revolutionizes the hiring process by automating and conducting interviews at scale. It offers a real-world interview simulator that adapts dynamically to candidates' responses, custom questions and metrics evaluation, cheating detection using eye, voice, and screen, and detailed reports for better hiring decisions. The platform enhances efficiency, candidate experience, and ensures security and integrity in the hiring process.

Scalesleek.app is a website that appears to have expired. It seems to have been a resource hub providing information on various topics. The domain is currently inactive, and users are advised to contact their domain registration service provider for assistance.

The website offers Full-Stack Cloud Observability services with a focus on Middleware. It provides comprehensive monitoring and analysis tools to ensure optimal performance and reliability of cloud-based applications. Users can gain insights into their middleware components and infrastructure to troubleshoot issues and improve overall system efficiency.

Allcancode is a platform that helps individuals and companies bring their product ideas to market quickly and efficiently. From idea-stage startups to established companies, Allcancode transforms ideas into marketable products by providing a devoted tech team, shortest time to market, staying on budget, and giving users access to their product's source code. The platform follows a lean process in 4 steps: Discovery & Design Phase, Development Phase, Delivery Phase, and Evolution Phase. Allcancode's secret sauce is its product development platform, offering development superpowers, the use of latest technologies, fully open source code, and easy and fast changes. Users can use the platform for free without limitations, ensuring full source code ownership.

Vendasta is a full-stack growth platform that provides agencies with the tools and resources they need to scale their businesses and help their clients succeed. With Vendasta, agencies can offer a wide range of services to their clients, including marketing automation, project management, sales and CRM, workflow automations, and more. Vendasta also offers a marketplace where agencies can resell products and services from other vendors, as well as a white-label client portal that allows clients to manage their own accounts and access all of the services that their agency offers.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

Ref Hub is an AI-powered online reference checking software based in Australia. The platform offers automated reference checks, fraud detection, lead generation tools, survey builder, and mobile app for managing reference checks on-the-go. With AI assessments, users can test candidates' skills at scale. Ref Hub provides flexible solutions for businesses of all sizes, from startups to large enterprises, streamlining the hiring process with reliable reference checks and smart assessments.

SolveCube is an integrated talent platform offering a suite of advisory, people strategy, and blended workforce solutions with AI technology. It provides tools and services for talent acquisition, business growth, partner referral programs, and more. SolveCube connects businesses with domain experts, offers permanent hiring solutions, employer of record services, recruiter on demand, lead generation partners, and ninja teams on demand. The platform combines AI and advanced technologies with domain expertise to revolutionize blended workforce strategies, ensuring accurate and efficient hiring processes. With a focus on business goals and needs, SolveCube delivers curated solutions for various industries and domains, enabling businesses to streamline their HR policies, assess workforce alignment, and scale their hiring efforts.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

AgentGPT is a platform that allows users to configure and deploy autonomous AI agents. Users can name their own custom AI and set it on any goal. The AI will think of tasks, execute them, and learn from the results to reach the goal. The platform provides a demo experience, automatic setup CLI, and a tech stack including Next.js, FastAPI, Prisma, TailwindCSS, Zod, and more. AgentGPT is designed to help users easily create and deploy AI agents for various tasks.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

Wenda is a platform for large-scale language model invocation designed to efficiently generate content for specific environments, considering the limitations of personal and small business computing resources, as well as knowledge security and privacy issues. The platform integrates capabilities such as knowledge base integration, multiple large language models for offline deployment, auto scripts for additional functionality, and other practical capabilities like conversation history management and multi-user simultaneous usage.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

The AI Engineering Hub is a repository that provides in-depth tutorials on LLMs and RAGs, real-world AI agent applications, and examples to implement, adapt, and scale in projects. It caters to beginners, practitioners, and researchers, offering resources for all skill levels to experiment and succeed in AI engineering.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

The Google Cloud Applied AI Engineering repository provides reference guides, blueprints, code samples, and hands-on labs developed by the Google Cloud Applied AI Engineering team. It contains resources for Generative AI on Vertex AI, including code samples and hands-on labs demonstrating the use of Generative AI models and tools in Vertex AI. Additionally, it offers reference guides and blueprints that compile best practices and prescriptive guidance for running large-scale AI/ML workloads on Google Cloud AI/ML infrastructure.

This repository contains the official codebase for Bittensor Subnet 1 (SN1) v1.0.0+, released on 22nd January 2024. It defines an incentive mechanism to create a distributed conversational AI for Subnet 1. Validators and miners are based on large language models (LLM) using internet-scale datasets and goal-driven behavior to drive human-like conversations. The repository requires python3.9 or higher and provides compute requirements for running validators and miners. Users can run miners or validators using specific commands and are encouraged to run on the testnet before deploying on the main network. The repository also highlights limitations and provides resources for understanding the architecture and methodology of SN1.

This repository contains the source code for the SIGIR 2023 paper 'Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited'. It provides resources for evaluating foundation, transferable, multi-modal, and LLM recommendation models, along with datasets, pre-trained models, and training strategies for IDRec and MoRec using in-batch debiased cross-entropy loss. The repository also offers large-scale datasets, code for SASRec with in-batch debias cross-entropy loss, and information on joining the lab for research opportunities.

Marlin is a highly optimized FP16xINT4 matmul kernel designed for large language model (LLM) inference, offering close to ideal speedups up to batchsizes of 16-32 tokens. It is suitable for larger-scale serving, speculative decoding, and advanced multi-inference schemes like CoT-Majority. Marlin achieves optimal performance by utilizing various techniques and optimizations to fully leverage GPU resources, ensuring efficient computation and memory management.

LLMLanding is a repository focused on practical implementation of large models, covering topics from theory to practice. It provides a structured learning path for training large models, including specific tasks like training 1B-scale models, exploring SFT, and working on specialized tasks such as code generation, NLP tasks, and domain-specific fine-tuning. The repository emphasizes a dual learning approach: quickly applying existing tools for immediate output benefits and delving into foundational concepts for long-term understanding. It offers detailed resources and pathways for in-depth learning based on individual preferences and goals, combining theory with practical application to avoid overwhelm and ensure sustained learning progress.

The Pinecone AWS Reference Architecture with Pulumi is a distributed system designed for vector-database-enabled semantic search over Postgres records. It serves as a starting point for specific use cases or as a learning resource. The architecture is permissively licensed and supported by Pinecone's open-source team, facilitating the setup of high-scale use cases for Pinecone's scalable vector database.

TPI-LLM (Tensor Parallelism Inference for Large Language Models) is a system designed to bring LLM functions to low-resource edge devices, addressing privacy concerns by enabling LLM inference on edge devices with limited resources. It leverages multiple edge devices for inference through tensor parallelism and a sliding window memory scheduler to minimize memory usage. TPI-LLM demonstrates significant improvements in TTFT and token latency compared to other models, and plans to support infinitely large models with low token latency in the future.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

BlueLM is a large-scale pre-trained language model developed by vivo AI Global Research Institute, featuring 7B base and chat models. It includes high-quality training data with a token scale of 26 trillion, supporting both Chinese and English languages. BlueLM-7B-Chat excels in C-Eval and CMMLU evaluations, providing strong competition among open-source models of similar size. The models support 32K long texts for better context understanding while maintaining base capabilities. BlueLM welcomes developers for academic research and commercial applications.

This repository focuses on organizing resources for building synthetic datasets using large language models. It covers important datasets, libraries, tools, tutorials, and papers related to synthetic data generation. The goal is to provide pragmatic and practical resources for individuals interested in creating synthetic datasets for machine learning applications.

Awesome-llm-role-playing-with-persona is a curated list of resources for large language models for role-playing with assigned personas. It includes papers and resources related to persona-based dialogue systems, personalized response generation, psychology of LLMs, biases in LLMs, and more. The repository aims to provide a comprehensive collection of research papers and tools for exploring role-playing abilities of large language models in various contexts.

Expert on Jelastic/Virtuozzo PaaS solutions.

all things snake

Optimizes production processes for efficient large-scale operations.

S&T Intelligence at Scale

Expert in full-scale app development, coding, and OCR.

I guide you towards cosmic-scale ideas that help advance humanity.

I help you build and scale Silicon Valley-style startups

Sequentially administers Big Five Test with 1-5 scale.

Rate AI-created model portraits on a 1-10 scale.

In-depth CIM analysis with a structured rating scale, offering detailed business evaluations.

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

Business Angel provides expert startup guidance: funding, growth hacks, and pitch advice. Navigate the startup ecosystem, from seed to scale. Essential for entrepreneurs aiming for success. Master your strategy and launch with confidence. Your startup journey begins here!

Proposes specific, scalable Fiverr gigs based on skills.

I help you with all your sysadmin tasks, from setting up your server to scaling your already exsisting one. I can help you with understanding the long list of log files and give you solutions to the problems.

Startup Strong Within 180 Days: Tailored advice for launching, promoting, and scaling businesses of all types. It covers all stages from pre-launch to post-launch and develops strategies including market research, branding, promotional tactics, and operational planning unique your business. (v1.8)

Learn Kubernetes by Hands-on Labs and AI

Expert in building Python web services on Render.

微生物学与酶工程专家，清华及麻省理工学者

Startup advisor guiding founders through detailed idea evaluation, product-market-fit, business model, GTM, and scaling.

gpt tools for scala coding