Best AI tools for< Deploy Ai Pipeline >

ML Clever is a no-code machine learning platform that empowers users to build powerful ML models with one click, explore what-if scenarios to guide decisions, and create interactive dashboards to explain results. It combines automated machine learning, interactive dashboards, and flexible prediction tools in one platform, allowing users to transform data into business insights without the need for data scientists or coding skills.

SiMa.ai is an AI application that offers high-performance, power-efficient, and scalable edge machine learning solutions for various industries such as automotive, industrial, healthcare, drones, and government sectors. The platform provides MLSoC™ boards, DevKit 2.0, Palette Software 1.2, and Edgematic™ for developers to accelerate complete applications and deploy AI-enabled solutions. SiMa.ai's Machine Learning System on Chip (MLSoC) enables full-pipeline implementations of real-world ML solutions, making it a trusted platform for edge AI development.

Hopsworks is an AI platform that offers a comprehensive solution for building, deploying, and monitoring machine learning systems. It provides features such as a Feature Store, real-time ML capabilities, and generative AI solutions. Hopsworks enables users to develop and deploy reliable AI systems, orchestrate and monitor models, and personalize machine learning models with private data. The platform supports batch and real-time ML tasks, with the flexibility to deploy on-premises or in the cloud.

Takomo.ai is a no-code AI builder that allows users to connect and deploy AI models in seconds. With Takomo.ai, users can combine the best AI models in a simple visual builder to create unique AI applications. Takomo.ai offers a variety of features, including a drag-and-drop builder, pre-trained ML models, and a single API call for accessing multi-model pipelines.

Plumb is a no-code, node-based builder that empowers product, design, and engineering teams to create AI features together. It enables users to build, test, and deploy AI features with confidence, fostering collaboration across different disciplines. With Plumb, teams can ship prototypes directly to production, ensuring that the best prompts from the playground are the exact versions that go to production. It goes beyond automation, allowing users to build complex multi-tenant pipelines, transform data, and leverage validated JSON schema to create reliable, high-quality AI features that deliver real value to users. Plumb also makes it easy to compare prompt and model performance, enabling users to spot degradations, debug them, and ship fixes quickly. It is designed for SaaS teams, helping ambitious product teams collaborate to deliver state-of-the-art AI-powered experiences to their users at scale.

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.

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.

SID is a data ingestion, storage, and retrieval pipeline that provides real-time context for AI applications. It connects to various data sources, handles authentication and permission flows, and keeps information up-to-date. SID's API allows developers to retrieve the right piece of data for a given task, enabling them to build AI apps that are fast, accurate, and scalable. With SID, developers can focus on building their products and leave the data management to SID.

FinetuneFast is an AI tool designed to help developers, indie makers, and businesses to efficiently finetune machine learning models, process data, and deploy AI solutions at lightning speed. With pre-configured training scripts, efficient data loading pipelines, and one-click model deployment, FinetuneFast streamlines the process of building and deploying AI models, saving users valuable time and effort. The tool is user-friendly, accessible for ML beginners, and offers lifetime updates for continuous improvement.

The Global Blockchain Show is an annual event that brings together experts and enthusiasts in the blockchain and AI industries. The event features a variety of speakers, workshops, and exhibitions, and provides a platform for attendees to learn about the latest developments in these fields. The 2024 Global Blockchain Show will be held in Dubai, UAE, from April 16-17. The event will feature a keynote address from Sophia, the world's most famous humanoid robot, as well as presentations from other leading experts in the blockchain and AI fields. Attendees will also have the opportunity to network with other professionals in the industry and learn about the latest products and services from leading companies. The Global Blockchain Show is a must-attend event for anyone interested in the latest developments in blockchain and AI.

SuperAnnotate is an AI data platform that simplifies and accelerates model-building by unifying the AI pipeline. It enables users to create, curate, and evaluate datasets efficiently, leading to the development of better models faster. The platform offers features like connecting any data source, building customizable UIs, creating high-quality datasets, evaluating models, and deploying models seamlessly. SuperAnnotate ensures global security and privacy measures for data protection.

Credal is an AI tool that allows users to build secure AI assistants for enterprise operations. It enables every employee to create customized AI assistants with built-in security, permissions, and compliance features. Credal supports data integration, access control, search functionalities, and API development. The platform offers real-time sync, automatic permissions synchronization, and AI model deployment with security and compliance measures. It helps enterprises manage ETL pipelines, schedule tasks, and configure data processing. Credal ensures data protection, compliance with regulations like HIPAA, and comprehensive audit capabilities for generative AI applications.

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.

Vectorize is a fast, accurate, and production-ready AI tool that helps users turn unstructured data into optimized vector search indexes. It leverages Large Language Models (LLMs) to create copilots and enhance customer experiences by extracting natural language from various sources. With built-in support for top AI platforms and a variety of embedding models and chunking strategies, Vectorize enables users to deploy real-time vector pipelines for accurate search results. The tool also offers out-of-the-box connectors to popular knowledge repositories and collaboration platforms, making it easy to transform knowledge into AI-generated content.

ConsciousML is a blog that provides in-depth and beginner-friendly content on machine learning, data engineering, and productivity. The blog covers a wide range of topics, including ML model deployment, data pipelines, deep work, data engineering, and more. The articles are written by experts in the field and are designed to help readers learn about the latest trends and best practices in machine learning and data engineering.

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.

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

Modjo is an AI platform designed to boost sales team productivity, enhance commercial efficiency, and develop sales skills. It captures interactions with customers, creates an augmented conversational database, and deploys AI solutions within organizations. Modjo's AI capabilities help in productivity, sales execution, and coaching, enabling teams to focus on selling, manage pipelines efficiently, and develop sales skills. The platform offers features like AI call scoring, CRM filling, call reviews, email follow-up, and insights library.

Outfit AI is an AI tool that enables users to design and deploy AI models or workflows as user-ready applications in minutes. It allows users to create custom user interfaces for their AI-powered apps by dropping in an API key from Replicate or Hugging Face. With Outfit AI, users can have creative control over the design of their apps, build complex workflows without any code, and optimize prompts for better performance. The tool aims to help users launch their models faster, save time, and enhance their AI applications with a built-in product copilot.

This is a unified DevOps platform to build AI applications. It provides a comprehensive set of tools and services to help developers build, deploy, and manage AI applications. The platform includes a variety of features such as a code editor, a debugger, a profiler, and a deployment manager. It also provides access to a variety of AI services, such as natural language processing, machine learning, and computer vision.

The End-to-End LLM Bootcamp is a comprehensive training program that covers the entire process of developing and deploying large language models. Participants learn to preprocess datasets, train models, optimize performance using NVIDIA technologies, understand guardrail prompts, and deploy AI pipelines using Triton Inference Server. The bootcamp includes labs, challenges, and practical applications, with a total duration of approximately 7.5 hours. It is designed for individuals interested in working with advanced language models and AI technologies.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

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.

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.

Awesome LLM Resources is a curated collection of resources for Large Language Models (LLMs) covering various aspects such as serverless hosting, accessing off-the-shelf models via API, local inference, LLM serving frameworks, open-source LLM web chat UIs, renting GPUs for fine-tuning, fine-tuning with no-code UI, fine-tuning frameworks, OS agentic/AI workflow, AI agents, co-pilots, voice API, open-source TTS models, OS RAG frameworks, research papers on chain-of-thought prompting, CoT implementations, CoT fine-tuned models & datasets, and more.

Welcome to the μLearn x Pathway Initiative, an exciting adventure into the world of Artificial Intelligence (AI)! This comprehensive course, developed in collaboration with Pathway, will empower you with the knowledge and skills needed to navigate the fascinating world of AI, with a special focus on Large Language Models (LLMs).

Elyra is a set of AI-centric extensions to JupyterLab Notebooks that includes features like Visual Pipeline Editor, running notebooks/scripts as batch jobs, reusable code snippets, hybrid runtime support, script editors with execution capabilities, debugger, version control using Git, and more. It provides a comprehensive environment for data scientists and AI practitioners to develop, test, and deploy machine learning models and workflows efficiently.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

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

Dify is an open-source LLM app development platform that combines AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more. It allows users to quickly go from prototype to production. Key features include: 1. Workflow: Build and test powerful AI workflows on a visual canvas. 2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions. 3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features. 4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval. 5. Agent capabilities: Define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools. 6. LLMOps: Monitor and analyze application logs and performance over time. 7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs for easy integration into your own business logic.

Vertex AI is a platform for end-to-end model development. It consist of core components that make the processes of MLOps possible for design patterns of all types.

The Google Cloud Vertex AI sample repository contains notebooks and community content that demonstrate how to develop and manage ML workflows using Google Cloud Vertex AI.

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.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

Dynamiq is an orchestration framework designed to streamline the development of AI-powered applications, specializing in orchestrating retrieval-augmented generation (RAG) and large language model (LLM) agents. It provides an all-in-one Gen AI framework for agentic AI and LLM applications, offering tools for multi-agent orchestration, document indexing, and retrieval flows. With Dynamiq, users can easily build and deploy AI solutions for various tasks.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

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