Xelora ecosystem
Last updated
Last updated
Our data engine consists of below tools/functions:
Multimodal - powered Data Crawler:
The Xelora platform boasts a cutting-edge feature: the Multimodal-powered Data Crawler, which is also enhanced by the integration of Chainlink Oracles to expand real-world data sources. Our multimodal-powered data crawler excels in gathering various formats such as images, text, videos, and audio, traffic camera footage, by deeply understanding and contextualizing your data needs. What sets our data crawler apart is its integration with a state-of-the-art multimodal Large Language Model (LLM) from Meta. This advanced LLM enables the crawler to understand the context of data, not just keywords. For example, if you want to gather specific image frames from USA traffic cameras, where it can identify and save frames containing trucks and cars, this crawler can help you well and automatically. The data, once crawled, is stored in a scalable vector database. This database is designed to automatically scale up, meeting the demands of large-scale crawling necessary for training your own LLMs. It ensures efficiency and continuity even in high-demand scenarios by automatically switching proxies.We will also leverage decentralized proxy resources to reduce proxy costs by up to 90% so that we can provide this tool for free for subscribed customers.
Auto Annotation:
Our platform offers a powerful tool for automatically labeling data, which is a huge time-saver in AI development. It leverages from pre-trained models to automatically label data. What's great about it is that it covers almost all areas of AI users might need, including Natural Language Processing (NLP), Computer Vision (CV), Automatic Speech Recognition (ASR), Large Language Models (LLM), etc.Using this auto-labeling tool is straightforward and seamless, even a non-technical person can do it.
Our platform introduces an innovative approach to model training with its Auto Model Learning and Active Learning feature. This function is designed to make AI models smarter and more accurate over time, with minimal effort from the user.Here's how it works: once any annotation is done or updated, the AI model is automatically trained.This continuous learning process is a game-changer. It ensures that your AI models are always evolving and becoming more precise, without the need for constant manual retraining. It's like having an AI that grows and improves with each task it performs, making your AI development process more efficient and effective.Overall, this feature streamlines the AI training process, saving time and resources while enhancing the quality of your AI models. It's an essential tool for anyone looking to develop highly accurate, state-of-the-art AI solutions.Additionally, Xelora also elevates AI model development with advanced distributed training and data parallelism capabilities.Key highlights include:
Auto-Scaling: Automatically scale computational resources across multiple GPU cards from multiple devices and connect into a decentralized supercomputer, adapting to the demands of your model training workload.
DeepSpeed-TED: a novel, three-dimensional, hybrid parallel algorithm that combines data, tensor, and expert parallelism to enable the training of MoE models with 4 to 8x larger base models than the current state-of-the-art. We follow and implement A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training.
The concept is crucial in the world of deep learning, where training complex models can take a significant amount of time. By parallelizing the training process across multiple devices (GPUs and/or CPUs) or even multiple machines, you can significantly reduce the overall training time, enabling you to iterate and experiment more rapidly.In addition to that, our platform is fully integrated with a suite of essential tools to facilitate end-to-end AI development. This includes seamless integration with Jupyter Notebook for interactive coding, Kubernetes for efficient container orchestration, a versatile demo environment for testing models, and Docker for consistent application deployment. These integrations ensure that AI builders have all the necessary tools at their fingertips to build, train, and deploy AI models efficiently and effectively on a single platform.
Our platform introduces a revolutionary approach to live model validation with a human monitoring AI system, underpinned by an on blockchain consensus-driven mechanism. This feature addresses a critical challenge in AI deployment: the degradation of model performance during live production. To ensure the highest standards of accuracy and reliability all the time, continuous validation of models in real-time is essential.Key aspects of this function include:
Seamless Process: Our system facilitates a smooth and seamless validation process, executed by a vast, globally distributed crowd. This ensures diverse and comprehensive monitoring of AI models in various real-world scenarios.
Blockchain Integration: By building this platform on blockchain technology, we leverage its inherent consensus mechanism to enhance the AI labeling and validation process. The decentralized nature of blockchain not only adds an extra layer of security but also ensures transparency and trustworthiness in the validation results.
Consensus-Driven Task Completion: Each validation task is randomly distributed to multiple verified contributors on our platform. The final validation result is recorded based on the consensus among these contributors, ensuring that only the most accurate and agreed-upon outcomes are accepted.
This approach guarantees that AI models remain robust and effective even in live production environments, addressing a key pain point in AI deployment and maintenance. By combining human intelligence with blockchain consensus, our platform sets a new standard for continuous, real-world AI model validation.
