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What is Deepbody Analytica?

We gather and analyze data from a wide array of sources, which includes socioeconomic variables, electronic health records, and biological samples like breath, blood, saliva, stool, urine, DNA, sweat, hair, and vital signs. This collection of health data offers valuable insights into your overall well-being. With the advent of advanced artificial intelligence, machine learning, and enhanced computational power, we are able to amalgamate information from diverse origins, providing your healthcare provider with a comprehensive view of your health risk factors. Our models are continually refined through the use of anonymized health data that is securely integrated into our database. The utilization of extensive data sets not only boosts the accuracy of our models but also significantly enhances AI's capability to detect health risks at an earlier stage compared to traditional methods, which can ultimately be life-saving. While many healthcare professionals primarily concentrate on diagnosing current health issues, our machine-learning models prioritize prevention and risk assessment, thus fostering a proactive healthcare approach. This paradigm shift enables us to identify and manage potential health challenges before they escalate into critical problems, ensuring better health outcomes for individuals. The proactive nature of our approach emphasizes the importance of early intervention in maintaining overall health.

What is Auger.AI?

Auger.AI offers a robust solution aimed at ensuring the accuracy of machine learning models. Our Machine Learning Review and Monitoring (MLRAM) tool plays a crucial role in keeping your models' precision consistent over time. It also evaluates the return on investment for your predictive analytics efforts! MLRAM's compatibility with any machine learning technology stack enhances its flexibility and adaptability. Failing to regularly assess the accuracy of your machine learning system lifecycle could lead to financial losses due to inaccurate predictions. Additionally, the ongoing requirement for model retraining can be expensive and may not resolve issues related to concept drift. MLRAM provides substantial advantages for both data scientists and business stakeholders, including features such as accuracy visualization graphs, performance alerts, anomaly detection, and automated optimized retraining. The process of integrating your predictive model with MLRAM is incredibly straightforward, requiring just a single line of code. We also offer a free one-month trial of MLRAM for qualifying users. With Auger.AI, you can leverage the most reliable AutoML platform available, empowering your organization to enhance its predictive capabilities to the fullest. This comprehensive approach ensures that your machine learning initiatives remain efficient and cost-effective over time.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
TensorFlow

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
TensorFlow

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$200 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Deepbody Analytica

Company Website

deepbody.me/

Company Facts

Organization Name

Auger.AI

Date Founded

2019

Company Location

United States

Company Website

auger.ai/

Categories and Features

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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