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Average Ratings 0 Ratings
Description
There is no need to purchase hardware, alter your existing network, or deal with complex installations; just a straightforward setup of a small software library is required. By the year 2025, it is projected that a staggering 75% of all data generated by enterprises, which amounts to 90 zettabytes, will originate from Internet of Things (IoT) devices. For context, the total storage capacity of every data center globally is currently less than two zettabytes. Additionally, an alarming 98% of IoT data remains unprotected, highlighting the urgent need for enhanced security across all data. One major challenge for IoT devices is the limited battery life of sensors, with few immediate solutions available. Moreover, many users of IoT face difficulties related to the range of wireless data transmission. We believe that AtomBeam will revolutionize the IoT landscape in much the same manner that electric lighting transformed daily life. The addition of our compaction software can effectively address several critical barriers to adopting IoT technologies. With just our software, you can enhance security measures, prolong sensor battery life, and boost transmission ranges significantly. AtomBeam also presents a chance to achieve considerable savings on connectivity and cloud storage expenses, facilitating a more efficient IoT ecosystem for all users. Ultimately, the integration of our software could reshape how businesses manage their data and optimize their resources.
Description
Scikit-learn offers a user-friendly and effective suite of tools for predictive data analysis, making it an indispensable resource for those in the field. This powerful, open-source machine learning library is built for the Python programming language and aims to simplify the process of data analysis and modeling. Drawing from established scientific libraries like NumPy, SciPy, and Matplotlib, Scikit-learn presents a diverse array of both supervised and unsupervised learning algorithms, positioning itself as a crucial asset for data scientists, machine learning developers, and researchers alike. Its structure is designed to be both consistent and adaptable, allowing users to mix and match different components to meet their unique requirements. This modularity empowers users to create intricate workflows, streamline repetitive processes, and effectively incorporate Scikit-learn into expansive machine learning projects. Furthermore, the library prioritizes interoperability, ensuring seamless compatibility with other Python libraries, which greatly enhances data processing capabilities and overall efficiency. As a result, Scikit-learn stands out as a go-to toolkit for anyone looking to delve into the world of machine learning.
API Access
Has API
API Access
Has API
Integrations
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NumPy
Integrations
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Matplotlib
ModelOp
NumPy
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
AtomBeam
Country
United States
Website
atombeamtech.com
Vendor Details
Company Name
scikit-learn
Country
United States
Website
scikit-learn.org/stable/
Product Features
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization