Key Technology

LLMOps

Model Builder

Model Builder is a solution for customers who want to adopt AI models in their services but face difficulties in model development. It allows for the easy and quick development of customer-specific language models. With features such as automatic dataset refinement, efficient parameter training, hyper-parameter optimization, and streamlined evaluation, customers can develop high-quality models in a time- and cost-efficient manner without the need for AI experts.

Model Builder is based on DeepAuto.ai's latest paper, ***Trirat et al., AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML, Preprint*.** AutoML-Agent is a novel multi-agent framework that automates the entire AI development pipeline, from data retrieval to model deployment, using specialized large language model agents to efficiently collaborate, plan, and verify tasks, enabling non-experts to build AI solutions with higher success and performance across various domains.

Model Builder is a solution for customers who want to adopt AI models in their services but face difficulties in model development. It allows for the easy and quick development of customer-specific language models. With features such as automatic dataset refinement, efficient parameter training, hyper-parameter optimization, and streamlined evaluation, customers can develop high-quality models in a time- and cost-efficient manner without the need for AI experts.

Model Builder is based on DeepAuto.ai's latest paper, ***Trirat et al., AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML, Preprint*.** AutoML-Agent is a novel multi-agent framework that automates the entire AI development pipeline, from data retrieval to model deployment, using specialized large language model agents to efficiently collaborate, plan, and verify tasks, enabling non-experts to build AI solutions with higher success and performance across various domains.

LLMOps

Model Compressor

Model Compressor is a solution that significantly reduces serving costs for customers already using AI models. It reduces memory usage by up to 80% and decreases computation, all without performance loss. The solution works using future-proof methods that find the optimal combination of parameter quantization and dimensionality reduction techniques, ensuring the best compression performance at all times.

Model Compressor is based on the Iterative Pareto-Optimal Candidate Search & Distillation method, which is patented by DeepAuto.ai. This technology operates by combining various quantization and pruning techniques to find the optimal solution, enabling it can be future-proof.

Model Compressor is a solution that significantly reduces serving costs for customers already using AI models. It reduces memory usage by up to 80% and decreases computation, all without performance loss. The solution works using future-proof methods that find the optimal combination of parameter quantization and dimensionality reduction techniques, ensuring the best compression performance at all times.

Model Compressor is based on the Iterative Pareto-Optimal Candidate Search & Distillation method, which is patented by DeepAuto.ai. This technology operates by combining various quantization and pruning techniques to find the optimal solution, enabling it can be future-proof.

LLMOps

Model Accelerator

Model Accelerator is designed to significantly improve the real-time performance of AI models for customers, while also saving on serving costs as throughput increases. It particularly shines when the input context size grows, offering superior speed compared to other services. By selectively excluding unnecessary information during the model's input and generation stages, it dramatically improves both speed and performance, enabling real-time services.

Model Accelerator is based on DeepAuto.ai's latest paper, Lee et al., "A Training-Free Sub-quadratic Cost Transformer Model Serving Framework with Hierarchically Pruned Attention," Preprint. In this paper, it is highlighted that handling long sequences is challenging due to high computational costs. However, the proposed Hierarchically Pruned Attention (HiP) method reduces both time and space complexity without requiring retraining, enabling efficient processing of millions of tokens on standard GPUs while maintaining high performance.

Model Accelerator is designed to significantly improve the real-time performance of AI models for customers, while also saving on serving costs as throughput increases. It particularly shines when the input context size grows, offering superior speed compared to other services. By selectively excluding unnecessary information during the model's input and generation stages, it dramatically improves both speed and performance, enabling real-time services.

Model Accelerator is based on DeepAuto.ai's latest paper, Lee et al., "A Training-Free Sub-quadratic Cost Transformer Model Serving Framework with Hierarchically Pruned Attention," Preprint. In this paper, it is highlighted that handling long sequences is challenging due to high computational costs. However, the proposed Hierarchically Pruned Attention (HiP) method reduces both time and space complexity without requiring retraining, enabling efficient processing of millions of tokens on standard GPUs while maintaining high performance.

LLMOps

Query Router

Query Router is a revolutionary solution that can reduce the API costs for customers using external language model APIs (e.g., GPT-4, Claude-3.5) by up to 90% without compromising response quality. It leverages small yet powerful open-source models (e.g., LLaMa-3.1 8B) and domain-specific models (e.g., AdaptLLM-Law) to improve both quality and cost-effectiveness. Moreover, it allows for custom routing model training, enabling more services to be provided.

Query Router is based on an optimal language model selection algorithm that uses a response quality prediction model for given queries, and this technology is patented by DeepAuto.ai. When a query is received, the Routing Engine projects it into a query-model cross-modal latent space, allowing for instant retrieval of the optimal model.

Query Router is a revolutionary solution that can reduce the API costs for customers using external language model APIs (e.g., GPT-4, Claude-3.5) by up to 90% without compromising response quality. It leverages small yet powerful open-source models (e.g., LLaMa-3.1 8B) and domain-specific models (e.g., AdaptLLM-Law) to improve both quality and cost-effectiveness. Moreover, it allows for custom routing model training, enabling more services to be provided.

Query Router is based on an optimal language model selection algorithm that uses a response quality prediction model for given queries, and this technology is patented by DeepAuto.ai. When a query is received, the Routing Engine projects it into a query-model cross-modal latent space, allowing for instant retrieval of the optimal model.

LLMOps

Model Evolver

Model Evolver is a solution that provides ongoing model management services for customers seeking stable AI model operation. It offers features such as performance monitoring, automatic performance improvements, automatic replacement with the latest optimal models, and continual learning with up-to-date data, ensuring performance improvement with minimal effort.

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Other Technologies

Model Hub & Dataset Hub

Explore the latest models & datasets

We periodically integrate the latest open-source models and datasets, offering intuitive visualization tools for effective analysis of key metrics, thereby enhancing the experience of exploring models and datasets.

Optimal Model Search

Find the perfect model for you

DeepAuto.ai's Model Search technology can route the user query to the optimal LLM model from a database of over 10K+ SOTA models in real-time, considering the complexity of the given query. It can also recommend the model with the lowest serving cost among the optimal models

Optimal Model Compression

Compress your model optimally

DeepAuto.ai's Model Compression technology can reduce the size of a given target model by up to 80% while minimizing performance loss. Additionally, by applying an efficient attention mechanism, it enables up to a 4x increase in generation speed.

PEFT & Hyper-parameter Optimization

Fine-tune your model efficiently

DeepAuto.ai's Finetuning technology significantly improves training time and cost through Parameter Efficient methods. Additionally, it integrates Hyper-parameter optimization to enable effective training.

Model Evaluation

Evaluate your model with ease

DeepAuto.ai's Evaluation feature allows you to effortlessly assess your model on important public or your own private benchmarks with just a few clicks. Utilize affordable computing infrastructure to evaluate and share your model.

Efficient & Stable Auto-scaled Serving

Serve your model at low-cost

DeepAuto.ai's Model Serving technology reliably and quickly serves optimally compressed models, offering the ability to automatically scale instances up or down in response to customer traffic. It provides a standardized API format to facilitate quick integration with customer applications.

Low-cost High-end GPUs & Data Storage

Launch your cloud workspace

DeepAuto.ai's Cloud Workspace offers high-end GPUs at an affordable price, enabling customers to train and test large AI models anytime, anywhere.

Dynamic Query Routing

Instantly route each question to the optimal model

DeepAuto.ai's Dynamic Query Routing technology instantly predicts the optimal model to answer a given user's question most accurately and appropriately. It then connects to the most cost-effective model, ensuring the highest quality while minimizing API costs.

Multi-modal Language AI

Capable of handling various input formats

DeepAuto.ai's Multimodal-Language AI supports various input formats, not just text. It enables question-answering from diverse input materials such as PDFs and images.

RAG Technology

Retrieve accurate data from Web,DB,etc

DeepAuto.ai's RAG technology is equipped with a retriever that explores various external data and a verifier that validates the retrieved data, providing accurate and verified answers.