Best AI tools for< Scale Workloads >

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

The website is Microsoft Azure, a cloud computing service offering a wide range of products and solutions for businesses and developers. Azure provides global infrastructure, FinOps, AI services, compute resources, containers, hybrid and multicloud solutions, analytics, application development, and more. It aims to empower users to innovate, modernize, and scale their applications and workloads efficiently on a secure and flexible cloud platform.

Gretel.ai is an AI tool that helps users incorporate generative AI into their data by generating synthetic data that is as good or better than the existing data. Users can fine-tune custom AI models and use Gretel's APIs to generate unlimited synthesized datasets, perform privacy-preserving transformations on sensitive data, and identify PII with advanced NLP detection. Gretel's APIs make it simple to generate anonymized and safe synthetic data, allowing users to innovate faster and preserve privacy while doing it. Gretel's platform includes Synthetics, Transform, and Classify APIs that provide users with a complete set of tools to create safe data. Gretel also offers a range of resources, including documentation, tutorials, GitHub projects, and open-source SDKs for developers. Gretel Cloud runners allow users to keep data contained by running Gretel containers in their environment or scaling out workloads to the cloud in seconds. Overall, Gretel.ai is a powerful AI tool for generating synthetic data that can help users unlock innovation and achieve more with safe access to the right data.

Airtrain is a no-code compute platform for Large Language Models (LLMs). It provides a user-friendly interface for fine-tuning, evaluating, and deploying custom AI models. Airtrain also offers a marketplace of pre-trained models that can be used for a variety of tasks, such as text generation, translation, and question answering.

Union.ai is an infrastructure platform designed for AI, ML, and data workloads. It offers a scalable MLOps platform that optimizes resources, reduces costs, and fosters collaboration among team members. Union.ai provides features such as declarative infrastructure, data lineage tracking, accelerated datasets, and more to streamline AI orchestration on Kubernetes. It aims to simplify the management of AI, ML, and data workflows in production environments by addressing complexities and offering cost-effective strategies.

SingleStore is a real-time data platform designed for apps, analytics, and gen AI. It offers faster hybrid vector + full-text search, fast-scaling integrations, and a free tier. SingleStore can read, write, and reason on petabyte-scale data in milliseconds. It supports streaming ingestion, high concurrency, first-class vector support, record lookups, and more.

UbiOps is an AI infrastructure platform that helps teams quickly run their AI & ML workloads as reliable and secure microservices. It offers powerful AI model serving and orchestration with unmatched simplicity, speed, and scale. UbiOps allows users to deploy models and functions in minutes, manage AI workloads from a single control plane, integrate easily with tools like PyTorch and TensorFlow, and ensure security and compliance by design. The platform supports hybrid and multi-cloud workload orchestration, rapid adaptive scaling, and modular applications with unique workflow management system.

RunPod is a cloud platform specifically designed for AI development and deployment. It offers a range of features to streamline the process of developing, training, and scaling AI models, including a library of pre-built templates, efficient training pipelines, and scalable deployment options. RunPod also provides access to a wide selection of GPUs, allowing users to choose the optimal hardware for their specific AI workloads.

Labelbox is a data factory platform that empowers AI teams to manage data labeling, train models, and create better data with internet scale RLHF platform. It offers an all-in-one solution comprising tooling and services powered by a global community of domain experts. Labelbox operates a global data labeling infrastructure and operations for AI workloads, providing expert human network for data labeling in various domains. The platform also includes AI-assisted alignment for maximum efficiency, data curation, model training, and labeling services. Customers achieve breakthroughs with high-quality data through Labelbox.

Raman Labs is an AI tool that offers dedicated modules for computer vision-based tasks. It allows users to integrate machine learning functionality into their existing applications with just 2 lines of code, ensuring real-time performance even with high-resolution data on consumer-grade CPUs. The API is clean and minimalistic, robust to large-scale and resolution variations, and versatile, running on Python3 and Numpy. The tool adapts to the computing power of the system, supporting both CPU and GPU for different workloads.

Scale AI is an AI tool that accelerates the development of AI applications for enterprise, government, and automotive sectors. It offers Scale Data Engine for generative AI, Scale GenAI Platform, and evaluation services for model developers. The platform leverages enterprise data to build sustainable AI programs and partners with leading AI models. Scale's focus on generative AI applications, data labeling, and model evaluation sets it apart in the AI industry.

Scale AI is an AI tool that accelerates the development of AI applications for various sectors including enterprise, government, and automotive industries. It offers solutions for training models, fine-tuning, generative AI, and model evaluations. Scale Data Engine and GenAI Platform enable users to leverage enterprise data effectively. The platform collaborates with leading AI models and provides high-quality data for public and private sector applications.

Content at Scale is an AI-powered content writing platform that helps businesses create high-quality, SEO-optimized content at scale. The platform offers a range of features, including: * **AI Writing Tools:** A suite of AI-powered writing tools that can be used to generate content for a variety of purposes, including blog posts, articles, website pages, and social media posts. * **Content Producer:** A tool that helps businesses create and plan fully optimized long-form content tailored to their brand voice. * **RankWell®:** An AI-powered SEO writing tool that helps businesses create content that is optimized for search engines and ranks well in search results. Content at Scale is used by a variety of businesses, including marketing agencies, publishers, and e-commerce businesses. The platform has helped businesses increase their website traffic, improve their search engine rankings, and generate more leads and sales.

Clay is a sales automation tool that helps businesses scale their outbound campaigns. It combines data from over 50 sources, web scraping, and AI messaging to enrich data and automate outbound processes. With Clay, businesses can build lead lists, enrich data, write personalized emails, and automate inbound leads. It offers a 14-day free trial and integrates with various tools and CRMs.

Clay is an AI-powered data enrichment and outreach automation tool designed to help go-to-market teams scale personalized outbound campaigns. It combines 75+ data enrichment tools, AI capabilities, and automation features to streamline lead generation, data cleaning, and personalized messaging. With access to 50+ data providers, Clay offers comprehensive coverage of information and enables users to connect, enrich, and sync their CRM data effortlessly. The platform also features AI web scraping, personalized email building, automated inbound and outbound processes, and data formatting functionalities.

Mava is a customer support platform that uses AI to help businesses manage their support operations. It offers a range of features, including a shared inbox, a Discord bot, and a knowledge base. Mava is designed to help businesses scale their support operations and improve their customer satisfaction.

Shotstack is a Cloud Video Editing API platform that offers AI-powered video creation capabilities. It allows users to create powerful video workflows and applications using AI and programmatic video editing tools. With features like bulk video editing, AI-powered video generation, scaled video rendering, and white-label video editor, Shotstack aims to streamline the video creation process for businesses and developers. The platform caters to various industries such as real estate, automotive, sports, and more, providing solutions for social media automation, video personalization, and embedded video editing. Shotstack also offers developer documentation, resources, and support to help users leverage its features effectively.

FinalScout is an AI-powered email finding and outreach tool that helps users extract valid email addresses from LinkedIn profiles and craft tailored emails with up to 98% deliverability. The tool enables users to scale their outreach efforts, connect with potential customers or clients, and prospect for new leads effortlessly. FinalScout also offers AI-powered email writing capabilities, making it easy to generate personalized emails using templates for various use cases. With features like a massive and accurate database, GDPR & CCPA compliance, and free trial options, FinalScout is a comprehensive solution for email outreach and prospecting.

ColdIQ is an AI-powered sales prospecting tool that helps B2B companies with revenue above $100k/month to build outbound systems that sell for them. The tool offers end-to-end cold outreach campaign setup and management, email infrastructure setup and warmup, audience research and targeting, data scraping and enrichment, campaigns optimization, sending automation, sales systems implementation, training on tools best practices, sales tools recommendations, free gap analysis, sales consulting, and copywriting frameworks. ColdIQ leverages AI to tailor messaging to each prospect, automate outreach, and flood calendars with opportunities.

Alli AI is an SEO automation and real-time deployment tool that helps agencies and SEO teams scale and automate their campaigns while saving time and money. With Alli AI, you can optimize your site, do keyword research, track your rankings, audit your site, and build backlinks all under one roof.

Beta9 is an open-source platform for running scalable serverless GPU workloads across cloud providers. It allows users to scale out workloads to thousands of GPU or CPU containers, achieve ultrafast cold-start for custom ML models, automatically scale to zero to pay for only what is used, utilize flexible distributed storage, distribute workloads across multiple cloud providers, and easily deploy task queues and functions using simple Python abstractions. The platform is designed for launching remote serverless containers quickly, featuring a custom, lazy loading image format backed by S3/FUSE, a fast redis-based container scheduling engine, content-addressed storage for caching images and files, and a custom runc container runtime.

Venice is a derived data storage platform, providing the following characteristics: 1. High throughput asynchronous ingestion from batch and streaming sources (e.g. Hadoop and Samza). 2. Low latency online reads via remote queries or in-process caching. 3. Active-active replication between regions with CRDT-based conflict resolution. 4. Multi-cluster support within each region with operator-driven cluster assignment. 5. Multi-tenancy, horizontal scalability and elasticity within each cluster. The above makes Venice particularly suitable as the stateful component backing a Feature Store, such as Feathr. AI applications feed the output of their ML training jobs into Venice and then query the data for use during online inference workloads.

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.

AwaDB is an AI native database designed for embedding vectors. It simplifies database usage by eliminating the need for schema definition and manual indexing. The system ensures real-time search capabilities with millisecond-level latency. Built on 5 years of production experience with Vearch, AwaDB incorporates best practices from the community to offer stability and efficiency. Users can easily add and search for embedded sentences using the provided client libraries or RESTful API.

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.

FastAgency is a powerful tool that leverages the AutoGen framework to quickly build applications with multi-agent workflows. It supports various interfaces like ConsoleUI and MesopUI, allowing users to create interactive applications. The tool enables defining workflows between agents, such as students and teachers, and summarizing conversations. FastAgency aims to expand its capabilities by integrating with additional agentic frameworks like CrewAI, providing more options for workflow definition and AI tool integration.

This repository provides a real-world trace dataset of LLM serving workloads for research and academic purposes. The dataset includes two files, BurstGPT.csv with trace data for 2 months including some failures, and BurstGPT_without_fails.csv without any failures. Users can scale the RPS in the trace, model patterns, and leverage the trace for various evaluations. Future plans include updating the time range of the trace, adding request end times, updating conversation logs, and open-sourcing a benchmark suite for LLM inference. The dataset covers 61 consecutive days, contains 1.4 million lines, and is approximately 50MB in size.

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings. The key features of LanceDB include: Production-scale vector search with no servers to manage. Store, query, and filter vectors, metadata, and multi-modal data (text, images, videos, point clouds, and more). Support for vector similarity search, full-text search, and SQL. Native Python and Javascript/Typescript support. Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index(*). Ecosystem integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB, and more on the way. LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.

SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution. SkyPilot abstracts away cloud infra burdens: - Launch jobs & clusters on any cloud - Easy scale-out: queue and run many jobs, automatically managed - Easy access to object stores (S3, GCS, R2) SkyPilot maximizes GPU availability for your jobs: * Provision in all zones/regions/clouds you have access to (the _Sky_), with automatic failover SkyPilot cuts your cloud costs: * Managed Spot: 3-6x cost savings using spot VMs, with auto-recovery from preemptions * Optimizer: 2x cost savings by auto-picking the cheapest VM/zone/region/cloud * Autostop: hands-free cleanup of idle clusters SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

Felafax is a framework designed to tune LLaMa3.1 on Google Cloud TPUs for cost efficiency and seamless scaling. It provides a Jupyter notebook for continued-training and fine-tuning open source LLMs using XLA runtime. The goal of Felafax is to simplify running AI workloads on non-NVIDIA hardware such as TPUs, AWS Trainium, AMD GPU, and Intel GPU. It supports various models like LLaMa-3.1 JAX Implementation, LLaMa-3/3.1 PyTorch XLA, and Gemma2 Models optimized for Cloud TPUs with full-precision training support.

GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM helps users gauge the performance, resource needs, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality. Key features include performance evaluation, resource optimization, cost estimation, and scalability testing.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

DataChain is an open-source Python library for processing and curating unstructured data at scale. It supports AI-driven data curation using local ML models and LLM APIs, handles large datasets, and is Python-friendly with Pydantic objects. It excels at optimizing batch operations and is designed for offline data processing, curation, and ETL. Typical use cases include Computer Vision data curation, LLM analytics, and validation.

LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

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.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

Synmetrix (prev. MLCraft) is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube (Cube.js) for flexible data models that consolidate metrics from various sources, enabling downstream distribution via a SQL API for integration into BI tools, reporting, dashboards, and data science. Use cases include data democratization, business intelligence, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

Synmetrix is an open source data engineering platform and semantic layer for centralized metrics management. It provides a complete framework for modeling, integrating, transforming, aggregating, and distributing metrics data at scale. Key features include data modeling and transformations, semantic layer for unified data model, scheduled reports and alerts, versioning, role-based access control, data exploration, caching, and collaboration on metrics modeling. Synmetrix leverages Cube.js to consolidate metrics from various sources and distribute them downstream via a SQL API. Use cases include data democratization, business intelligence and reporting, embedded analytics, and enhancing accuracy in data handling and queries. The tool speeds up data-driven workflows from metrics definition to consumption by combining data engineering best practices with self-service analytics capabilities.

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.

Expert on Jelastic/Virtuozzo PaaS solutions.

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

SALE OF USED CARS. Purchase of cars from the population and organizations