Clarifai AI technology page Top Builders

Explore the top contributors showcasing the highest number of Clarifai AI technology page app submissions within our community.

Clarifai

Clarifai's groundbreaking technology aims to bring The World’s AI(™) to developers and data scientists around the globe. One of the first deep learning platforms since its establishment in 2013 by Matthew Zeiler, Ph.D, a distinguished expert in the realm of machine learning, the company has consistently set high benchmarks in the AI industry.

Prior to founding Clarifai, Dr. Zeiler took the top five places in image classification at the 2013 ImageNet Challenge. By 2019, Clarifai had made such a significant impact that it was recognized as a leader in Forrester’s New Wave Computer Vision Platforms report, making it the sole startup to attain a differentiated rating. In 2022 Clarifai launched a new free service – Clarifai Community – designed to empower everyone to create, share and use The World's AI. That same year Clarifai further solidified its reputation when IDC MarketScape acknowledged it as a leader in Computer Vision AI Platforms. In 2023, Gartner named Clarifai a niche player in the Magic Quadrant for Cloud AI Developer Services for the third year in a row.

Catering to the entire AI developmental life cycle, today Clarifai provides an AI platform for unstructured image, video, text, and audio data. The platform supports data exploration, data labeling, model training, evaluation and inference, and deployment. Clarifai enables users to implement AI technology into their products via API, Mobile SDK, and/or on-premise solutions.

General

Platform: https://www.clarifai.com/
Release date: 2013
Founder: Matthew Zeiler, Ph.D.
Type: AI-powered software platform
Achievements: Winner of the top five places in image classification at the ImageNet 2013 competition, Recognized as a leader in Forrester’s New Wave Computer Vision Platforms in 2019, Computer Vision AI Platforms by IDC MarketScape in 2022, and Gartner CAIDS ‘21-’23.

Flexible Deployment

Clarifai is known for its flexible deployment options, allowing users to choose from an array of platforms, be it any cloud such as AWS, Azure, or Google Cloud, or on bare-metal with options for air-gapped setups and on-edge deployments.

AI Lake

Clarifai's AI Lake is designed to enhance AI adoption and its reusability across enterprises. It has features such as:

Storing and analyzing vast amounts of data objects.
Auto-indexing your data using AI.
Tracking the lineage and versioning of all AI assets.

Centralized and Secure AI

The platform offers centralized and secure AI functionalities that employ foundation models to simplify the machine learning model development and deployment process. It provides an efficient structure to organize, share, and reuse all AI resources.

Data Labeling with Scribe

Data labeling has never been more streamlined:

Automated data labeling upon data ingestion.
Scalability to cater to labeling projects of any magnitude.
Intuitive labeling and reviewer user interfaces to optimize the quality and quantity of annotations.

Open, Reusable Building Blocks

Clarifai’s fully-integrated AI platform brings forth a wide variety of machine learning products and services that seamlessly collaborate to produce AI on an impressive scale:

AI Lake: A central repository for all AI assets.
Spacetime: Enables vector and input searching.
Enlight: Fine-tune any open-source or third-party model with ease.
**Mesh: **Connect models to create more complex logic.
Armada: Instantaneously deploy your models for production.
Modules: Effortlessly deploy Streamlit-powered UIs in your apps or on top of Clarifai's Portal.

Start Building with Clarifai

Clarifai provides an extensive range of libraries and resources to assist developers in embarking on their AI journey. Dive into a vibrant ecosystem and see what others are creating using Clarifai’s cutting-edge technology. For a deeper understanding and tutorials, explore the dedicated sections on Clarifai's official website.

Documentation for developers

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Clarifai AI technology page Hackathon projects

Discover innovative solutions crafted with Clarifai AI technology page, developed by our community members during our engaging hackathons.

Fronto

Fronto is a revolutionary AI-powered website builder designed to simplify the website creation process for individuals and businesses. With innovative features like "ScreenShot to code" and "Image Link to code," Fronto eliminates the need for manual coding and design, allowing users to create professional websites quickly and easily. Leveraging advanced AI assistants like GPT-4 and GPT-4 Vision and another models, Fronto automates the entire process, from design inspiration to functional code generation. Trulens provides feedback and evaluates the performance of our OpenAI assistants, ensuring the effectiveness and reliability of Fronto's AI-powered features. Whether you're a novice or an experienced developer, Fronto empowers you to bring your website ideas to life effortlessly.

Adapt-a-RAG

Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.

comedian-joke-book

Comedian Joke Book is a groundbreaking platform that combines the power of AI with the creativity of humor enthusiasts worldwide. Submit your best original jokes and let our advanced natural language processing categorize them by theme, structure and style. Explore the resulting interactive joke visualization to discover new talent, uncover emerging trends, and find inspiration for your own comedy writing. Most importantly, JokeMap is dedicated to protecting the intellectual property of humor creators. Our originality scoring engine compares new submissions against our vast database, flagging potential plagiarism and giving proper attribution. With a transparent, immutable record of each joke's origin and timeline, JokeMap aims to become the trusted home for comedic talent everywhere. Whether you're an aspiring comedian, a prolific humor writer, or simply a connoisseur of clever jokes, JokeMap provides the tools and community to help you explore, create and preserve the very best in original comedy. Join us on this exciting journey as we map the frontiers of humor!

Storyforge

Experience the future of game development with StoryForge, an innovative AI application created to empower game developers and unleash your creative potential. With StoryForge, crafting captivating game design documents becomes effortless and enjoyable. Dive into the depths of creativity as you shape immersive storylines, meticulously crafting intricate plots that keep players on the edge of their seats. Define compelling protagonists and antagonists, breathing life into their unique personalities and motivations. With the power of AI-generated images, StoryForge allows you to visualize breathtaking environments that transport players to mesmerizing worlds. From lush landscapes to futuristic cityscapes, bring your game concepts to life with stunning visuals. Say goodbye to the complexities of game design as StoryForge streamlines the entire process, providing a user-friendly interface. Unleash your storytelling potential and embark on a thrilling journey of game creation with StoryForge at your side. The possibilities are endless, and the adventure awaits.

LexaScan

According to a study by McKinsey, employees spend almost 2 hours per day searching and gathering information. Manual extraction processes cannot scale to handle large volumes of data. Automated data extraction solves the inefficiencies of manual information gathering. LexaScan is an AI solution that extracts data from text and images, delivering structured JSON format. We used GPT-4 Vision from OpenAI to extract data from text and images. We also used TruLens to ensure that the output is relevant and harmless. The total addressable market for data extraction software was estimated at $1.4 billion US dollars in 2022 and is expected to reach $3.8 billion dollars by 2028. Conservatively, we estimate the serviceable addressable market to be $140 million and the serviceable obtainable market to be $42 million.

AI Image Categorizer

This application is the solution to the lack of specific data collected by visual data. Using Google Gemini's model, we have mapped tags to images. New this generated data can be vectorized and search for, meaning the most computationally expensive operation can be done per image, and the tags can be searched for using sematic search rather than collecting matching tags.