Provides a machine learning framework that generates statistically optimized data embeddings, improving model performance on sparse, high-dimensional, or limited datasets without extensive feature engineering. By creating richer representations of complex data relationships, it enables faster training and more accurate predictions across various domains, including finance, healthcare, and e-commerce.
Funding
$4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Training machine learning models on datasets that are sparse, high-dimensional, or limited in size often results in suboptimal performance, requiring extensive feature engineering and computational resources. Existing methods for improving model accuracy on such data can be complex, time-consuming, and may not fully capture the underlying relationships within the data.
Solution
Ensemble AI offers a model compression and optimization platform that generates statistically optimized data embeddings, enabling data scientists to train accurate ML models on imperfect data without extensive feature engineering. The platform seamlessly integrates into existing ML pipelines, either on-premises or via cloud API, and is compatible with various data modalities, including LLMs, vision, speech, and multimodal data. By creating richer representations of complex data relationships, Ensemble AI improves model performance, speeds up training, and reduces inference costs, all while maintaining data privacy and control. The platform supports popular model formats like ONNX, PyTorch, and TensorFlow, allowing users to compress models and deploy them to various hardware environments, including edge devices, CPUs, and mobile devices.
Target Audience
The primary target audience includes data scientists, machine learning engineers, and AI developers who need to optimize the performance of their models on limited, sparse, and high-dimensional data, as well as organizations looking to reduce the computational costs and latency associated with deploying large AI models.
Features
- Model shrinking platform that compresses AI models without sacrificing accuracy
- Compatibility with any model and any data modality (LLMs, vision, speech, multimodal)
- Support for popular model formats like ONNX, PyTorch, and TensorFlow
- Seamless integration into existing ML pipelines, either on-premises or via cloud API
- Capability to maintain or improve model accuracy while reducing size and latency
- NdLinear architecture, an open-source, drop-in replacement for traditional linear layers that reduces parameter counts and FLOPs
- Self-serve platform for compressing models under 1B parameters
- Enterprise edition for 1B+ parameters, custom workflows, and on-prem deployments