AI video tagging used to mean manual review and basic object detection. With multimodal models and dynamic taxonomies, you can now automatically detect brand moments, inappropriate content, actions, moods and trending content at scale.
This guide will walk developers through building a modern Media Asset Management (MAM) system with semantic search capabilities using Mixpeek's infrastructure.
Intelligent video chunking using scene detection and vector embeddings. This tutorial covers how to break down videos into semantic scenes, generate embeddings, and enable powerful semantic search capabilities.
AI-powered image discovery app using Mixpeek's multimodal SDK and MongoDB's $vectorSearch. Features deep learning, vector embeddings, and KNN search for advanced visual content management.
This article demonstrates how to build a reverse video search system using Mixpeek for video processing and embedding, and Weaviate as a vector database, enabling both video and text queries to find relevant video segments through semantic similarity.
Our brains process multiple inputs simultaneously. Mixpeek brings this power to AI, enabling multimodal video understanding. Search across transcripts, visuals, and more for truly intelligent content analysis. #AI #VideoAnalytics
At Mixpeek, we're on a mission to make multimodal search (images, videos, audio and text) accessible and powerful.
Find, analyze, and leverage visual information within your video library using advanced AI and natural language processing, revolutionizing how you interact with and extract value from your multimedia assets.
Building a Comprehensive Image Indexing, Retrieval, and Generation Pipeline Using Mixpeek and Replicate's FLUX
Streamline your content management with Mixpeek’s Multimodal Classification. Automatically categorize videos, images, audio files, and text into predefined categories, making data retrieval faster and more efficient. Ideal for businesses handling diverse content types.
Automatic, AI-generated video captioning for video
Here's a look at the top 5 most impactful papers, their significance, and their implications for the future.
From pristine offices to high-risk buildings, see how smart visual analysis is saving insurers millions.
Build a scalable, distributed video processing pipeline using celery and render with fastapi
In the ever-evolving landscape of digital content, the ability to process vast amounts of unstructured data has become a game-changer.
In today's data-driven world, insurance companies are constantly seeking innovative ways to assess risk accurately and efficiently. Mixpeek
Build a multimodal data processing pipeline using Apache Kafka, Apache Airflow, and Amazon SageMaker. This pipeline will handle various file types (image, video, audio, text, and documents) in parallel, process them through custom ML tasks, and store the results in a database.
How to deploy and run OpenAI's CLIP model on Amazon SageMaker for efficient real-time and offline inference.
Reverse video search allows us to use a video clip as an input for a query against videos that have been indexed in a vector store.
Using semantic video understanding models to intelligently locate key scenes across petabytes of videos.
State-of-the art video understanding model that converts videos into embeddings.
Unlock the power of your unstructured data with Mixpeek, automating ETL from S3 to MongoDB and enabling advanced question answering, content analysis, and semantic search capabilities through LangChain's cutting-edge AI models.
The standard design pattern when you want to serve non JSON data to your client is to first store it
Semantic video understanding bridges the gap of labeling, enabling a complete analysis of video content.
Learn best practices, reference architectures and follow example tutorials to build multimodal AI applications
