Best AI tools for< Build Ml Models >

UnfoldAI is a website offering articles, strategies, and tutorials for building production-grade ML systems. Authored by Simeon Emanuilov, the site covers topics such as deep learning, computer vision, LLMs, programming, MLOps, performance, scalability, and AI consulting. It aims to provide insights and best practices for professionals in the field of machine learning to create robust, efficient, and scalable systems.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

JFrog ML is an AI platform designed to streamline AI development from prototype to production. It offers a unified MLOps platform to build, train, deploy, and manage AI workflows at scale. With features like Feature Store, LLMOps, and model monitoring, JFrog ML empowers AI teams to collaborate efficiently and optimize AI & ML models in production.

Rapid AI DAta Yields (RAIDAY) is a platform that provides AI tools, data products, and educational resources to help businesses and individuals leverage the power of artificial intelligence. RAIDAY's mission is to democratize and streamline the creation of simple yet powerful AI and data products for everyone, regardless of their technical expertise or resources. The platform offers a range of AI tools, including content generators, data analysis tools, and AI-powered chatbots. RAIDAY also provides a library of AI-generated content and data products that can be used to train AI models or to create new AI applications. In addition to its AI tools and data products, RAIDAY also offers a variety of educational resources, including tutorials, webinars, and blog posts, to help users learn about AI and how to use it effectively.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Teraflow.ai is an AI-enablement company that specializes in helping businesses adopt and scale their artificial intelligence models. They offer services in data engineering, ML engineering, AI/UX, and cloud architecture. Teraflow.ai assists clients in fixing data issues, boosting ML model performance, and integrating AI into legacy customer journeys. Their team of experts deploys solutions quickly and efficiently, using modern practices and hyper scaler technology. The company focuses on making AI work by providing fixed pricing solutions, building team capabilities, and utilizing agile-scrum structures for innovation. Teraflow.ai also offers certifications in GCP and AWS, and partners with leading tech companies like HashiCorp, AWS, and Microsoft Azure.

Hugging Face is an AI community platform that facilitates collaboration on models, datasets, and applications within the machine learning community. It offers a wide range of tools and resources for developers and researchers to create, discover, and share machine learning projects. The platform aims to accelerate the development of AI technologies and foster innovation in the field of artificial intelligence.

Altamira is an AI-driven software development company that offers a wide range of services including software discovery, ideation, audit, consulting, and development. They specialize in AI feasibility studies, AI development, dataOps pipelines, and pre-built AI/ML models. Altamira focuses on providing holistic care for digital solutions, with expertise in various industries such as fintech, retail, healthcare, and more. They aim to optimize software development processes for established businesses, startups, and spinoffs by offering tailored solutions that make a tangible impact on growth and productivity.

TensorFlow is an end-to-end platform for machine learning. It provides a wide range of tools and resources to help developers build, train, and deploy ML models. TensorFlow is used by researchers and developers all over the world to solve real-world problems in a variety of domains, including computer vision, natural language processing, and robotics.

Streamlit is an open-source Python library that makes it easy to create and share beautiful and interactive web apps for data science and machine learning.

Plat.AI is an automated predictive analytics software that offers model building solutions for various industries such as finance, insurance, and marketing. It provides a real-time decision-making engine that allows users to build and maintain AI models without any coding experience. The platform offers features like automated model building, data preprocessing tools, codeless modeling, and personalized approach to data analysis. Plat.AI aims to make predictive analytics easy and accessible for users of all experience levels, ensuring transparency, security, and compliance in decision-making processes.

ClearML is an open-source, end-to-end platform for continuous machine learning (ML). It provides a unified platform for data management, experiment tracking, model training, deployment, and monitoring. ClearML is designed to make it easy for teams to collaborate on ML projects and to ensure that models are deployed and maintained in a reliable and scalable way.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

Comet ML is an extensible, fully customizable machine learning platform that aims to move ML forward by supporting productivity, reproducibility, and collaboration. It integrates with existing infrastructure and tools to manage, visualize, and optimize models from training runs to production monitoring. Users can track and compare training runs, create a model registry, and monitor models in production all in one platform. Comet's platform can be run on any infrastructure, enabling users to reshape their ML workflow and bring their existing software and data stack.

Duckietown is a platform for delivering cutting-edge robotics and AI learning experiences. It offers teaching resources to instructors, hands-on activities to learners, an accessible research platform to researchers, and a state-of-the-art ecosystem for professional training. Duckietown's mission is to make robotics and AI education state-of-the-art, hands-on, and accessible to all.

Metaflow is an open-source framework for building and managing real-life ML, AI, and data science projects. It makes it easy to use any Python libraries for models and business logic, deploy workflows to production with a single command, track and store variables inside the flow automatically for easy experiment tracking and debugging, and create robust workflows in plain Python. Metaflow is used by hundreds of companies, including Netflix, 23andMe, and Realtor.com.

Voxel51 is an AI tool that provides open-source computer vision tools for machine learning. It offers solutions for various industries such as agriculture, aviation, driving, healthcare, manufacturing, retail, robotics, and security. Voxel51's main product, FiftyOne, helps users explore, visualize, and curate visual data to improve model performance and accelerate the development of visual AI applications. The platform is trusted by thousands of users and companies, offering both open-source and enterprise-ready solutions to manage and refine data and models for visual AI.

Striveworks is an AI application that offers a Machine Learning Operations Platform designed to help organizations build, deploy, maintain, monitor, and audit machine learning models efficiently. It provides features such as rapid model deployment, data and model auditability, low-code interface, flexible deployment options, and operationalizing AI data science with real returns. Striveworks aims to accelerate the ML lifecycle, save time and money in model creation, and enable non-experts to leverage AI for data-driven decisions.

MLflow is an open source platform for managing the end-to-end machine learning (ML) lifecycle, including tracking experiments, packaging models, deploying models, and managing model registries. It provides a unified platform for both traditional ML and generative AI applications.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

The Machine Learning Road Map is a comprehensive guide designed to take individuals from various levels of machine learning knowledge to a basic understanding of machine learning principles using high-quality, free resources. It aims to simplify the complex and rapidly growing field of machine learning by providing a structured roadmap for learning. The guide emphasizes the importance of understanding AI for everyone, the need for patience in learning machine learning due to its complexity, and the value of learning from experts in the field. It covers five different paths to learning about machine learning, catering to consumers, aspiring AI researchers, ML engineers, developers interested in building ML applications, and companies looking to implement AI solutions.

AI System School is a curated list of research in machine learning systems, focusing on ML/DL infra, LLM infra, domain-specific infra, ML/LLM conferences, and general resources. It provides resources such as data processing, training systems, video systems, autoML systems, and more. The repository aims to help users navigate the landscape of AI systems and machine learning infrastructure, offering insights into conferences, surveys, books, videos, courses, and blogs related to the field.

Netsaur is a powerful machine learning library for Deno, offering a lightweight and easy-to-use neural network solution. It is blazingly fast and efficient, providing a simple API for creating and training neural networks. Netsaur can run on both CPU and GPU, making it suitable for serverless environments. With Netsaur, users can quickly build and deploy machine learning models for various applications with minimal dependencies. This library is perfect for both beginners and experienced machine learning practitioners.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

KitOps is a packaging and versioning system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using. KitOps simplifies the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. KitOps' ModelKits are a standards-based package for models, their dependencies, configurations, and codebases. ModelKits are portable, reproducible, and work with the tools you already use.

Giskard is an open-source Python library that automatically detects performance, bias & security issues in AI applications. The library covers LLM-based applications such as RAG agents, all the way to traditional ML models for tabular data.

ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference. It aims to offer simple, high-performance ML inference and a good developer experience. Users can provide inference APIs for ONNX models without writing additional code by placing the models in the directory structure. Each session can choose between CPU or CUDA, analyze input/output, and provide Swagger API documentation for easy testing. Ready-to-run Docker images are available, making it convenient to deploy the server.

The Vertex AI SDK for Python is a library that provides a convenient way to use the Vertex AI API. It offers a high-level interface for creating and managing Vertex AI resources, such as datasets, models, and endpoints. The SDK also provides support for training and deploying custom models, as well as using AutoML models. With the Vertex AI SDK for Python, you can quickly and easily build and deploy machine learning models on Vertex AI.

Cyclops is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely: data - Create datasets for training, inference and evaluation. We use the popular 🤗 datasets to efficiently load and slice different modalities of data models - Use common model implementations using scikit-learn and PyTorch tasks - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data evaluate - Evaluate models on clinical prediction tasks monitor - Detect dataset shift relevant for clinical use cases report - Create model report cards for clinical ML models

AIShield Watchtower is a tool designed to fortify the security of AI/ML models and Jupyter notebooks by automating model and notebook discoveries, conducting vulnerability scans, and categorizing risks into 'low,' 'medium,' 'high,' and 'critical' levels. It supports scanning of public GitHub repositories, Hugging Face repositories, AWS S3 buckets, and local systems. The tool generates comprehensive reports, offers a user-friendly interface, and aligns with industry standards like OWASP, MITRE, and CWE. It aims to address the security blind spots surrounding Jupyter notebooks and AI models, providing organizations with a tailored approach to enhancing their security efforts.

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform. With Flyte, user teams can construct pipelines using the Python SDK, and seamlessly deploy them on both cloud and on-premises environments, enabling distributed processing and efficient resource utilization.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

Resonance is a framework designed to facilitate interoperability and messaging between services in your infrastructure and beyond. It provides AI capabilities and takes full advantage of asynchronous PHP, built on top of Swoole. With Resonance, you can: * Chat with Open-Source LLMs: Create prompt controllers to directly answer user's prompts. LLM takes care of determining user's intention, so you can focus on taking appropriate action. * Asynchronous Where it Matters: Respond asynchronously to incoming RPC or WebSocket messages (or both combined) with little overhead. You can set up all the asynchronous features using attributes. No elaborate configuration is needed. * Simple Things Remain Simple: Writing HTTP controllers is similar to how it's done in the synchronous code. Controllers have new exciting features that take advantage of the asynchronous environment. * Consistency is Key: You can keep the same approach to writing software no matter the size of your project. There are no growing central configuration files or service dependencies registries. Every relation between code modules is local to those modules. * Promises in PHP: Resonance provides a partial implementation of Promise/A+ spec to handle various asynchronous tasks. * GraphQL Out of the Box: You can build elaborate GraphQL schemas by using just the PHP attributes. Resonance takes care of reusing SQL queries and optimizing the resources' usage. All fields can be resolved asynchronously.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

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