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features
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Description

Illuminate the black box and install 3LC to acquire the insights necessary for implementing impactful modifications to your models in no time. Eliminate uncertainty from the training process and enable rapid iterations. Gather metrics for each sample and view them directly in your browser. Scrutinize your training process and address any problems within your dataset. Engage in model-driven, interactive data debugging and improvements. Identify crucial or underperforming samples to comprehend what works well and where your model encounters difficulties. Enhance your model in various ways by adjusting the weight of your data. Apply minimal, non-intrusive edits to individual samples or in bulk. Keep a record of all alterations and revert to earlier versions whenever needed. Explore beyond conventional experiment tracking with metrics that are specific to each sample and epoch, along with detailed data monitoring. Consolidate metrics based on sample characteristics instead of merely by epoch to uncover subtle trends. Connect each training session to a particular dataset version to ensure complete reproducibility. By doing so, you can create a more robust and responsive model that evolves continuously.

Description

EDMS enhances the efficiency and cost-effectiveness of environmental data collection, management, and reporting while ensuring standardization across the board. By establishing a clear, documented, and auditable procedure for collecting, storing, and reporting environmental data, it streamlines the entire process. All phases of sampling and analysis are meticulously tracked, and the system is capable of sending notifications via email or SMS for various purposes. Automatic alerts are generated for instances of non-compliance and sample exceedances, further enhancing accountability. The initial phase of any environmental field initiative involves careful scheduling and planning of sampling events, which EDMS facilitates by enabling project managers to design, assign, and oversee these events. These sampling events can be organized as one-time occurrences or scheduled regularly. Before data collection begins, project managers can identify sampling stations, which come with detailed information about their locations, construction, sampling defaults and methodologies, required laboratory analyses, necessary equipment, and personnel involved. By incorporating these features, EDMS not only simplifies the process but also ensures that all stakeholders are well-informed and prepared for successful data collection.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
Apache Arrow Yes 
Apache Parquet Yes 
Google Cloud Platform Yes 
Google Colab Yes 
Hugging Face Yes 
Jupyter Notebook Yes 
Microsoft Azure Yes 
NumPy Yes 
PyTorch Yes 
Python Yes 
pandas Yes 

Integrations

Amazon Web Services (AWS) No 
Apache Arrow No 
Apache Parquet No 
Google Cloud Platform No 
Google Colab No 
Hugging Face No 
Jupyter Notebook No 
Microsoft Azure No 
NumPy No 
PyTorch No 
Python No 
pandas No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$1,695 per license
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

3LC

Website

3lc.ai/

Vendor Details

Company Name

GAEA Technologies

Founded

1994

Country

Canada

Website

www.gaea.ca/edms.php

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Environmental

Air Management No 
Auditing No 
Compliance Management No 
Document Management No 
Emissions Inventory No 
Hazardous Materials Control No 
Sustainability Management No 
Training Management No 
Waste Management No 
Water Management No 

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