Ensemble
About Ensemble
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.
```xml <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. </problem> <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. </solution> <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 </features> <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. </target_audience> <revenue_model> Ensemble AI offers a self-serve platform and an enterprise edition with custom contracts for higher volumes and specialized needs. </revenue_model> ```
What does Ensemble do?
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.
Where is Ensemble located?
Ensemble is based in San Francisco, United States.
When was Ensemble founded?
Ensemble was founded in 2023.
How much funding has Ensemble raised?
Ensemble has raised 3950000.
Who founded Ensemble?
Ensemble was founded by Alex Reneau.
- Alex Reneau - Co-founder/CEO
- Location
- San Francisco, United States
- Founded
- 2023
- Funding
- 3950000
- Employees
- 6 employees
- Major Investors
- Salesforce Ventures