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Description
Actcast is a cutting-edge edge-AI IoT platform service that seamlessly connects real-world events and data to the Internet by executing deep learning inference on edge devices, which facilitates immediate sensing, analysis, and assimilation of physical data with online systems while minimizing both data transfer expenses and privacy concerns. By leveraging edge computing, it allows for the execution of deep learning models directly on affordable hardware like Raspberry Pi, transforming raw inputs from sensors and cameras into meaningful, semantic information that can be relayed to web services or applications. The platform is designed to support the deployment, remote management, and monitoring of IoT applications, referred to as "Acts," across a variety of devices, offering developers essential tools such as an SDK and command-line interface for creating, packaging, and deploying applications within Docker containers that analyze input and deliver condensed outputs. Furthermore, Actcast features capabilities for organizing device groups, setting up triggers and webhooks for event notifications, and managing updates and device statuses through a unified dashboard, ensuring a more streamlined and efficient IoT experience. This comprehensive approach not only enhances operational efficiency but also improves the scalability of IoT solutions.
Description
Amazon Elastic Inference provides an affordable way to enhance Amazon EC2 and Sagemaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference costs by as much as 75%. It is compatible with models built on TensorFlow, Apache MXNet, PyTorch, and ONNX. The term "inference" refers to the act of generating predictions from a trained model. In the realm of deep learning, inference can represent up to 90% of the total operational expenses, primarily for two reasons. Firstly, GPU instances are generally optimized for model training rather than inference, as training tasks can handle numerous data samples simultaneously, while inference typically involves processing one input at a time in real-time, resulting in minimal GPU usage. Consequently, relying solely on GPU instances for inference can lead to higher costs. Conversely, CPU instances lack the necessary specialization for matrix computations, making them inefficient and often too sluggish for deep learning inference tasks. This necessitates a solution like Elastic Inference, which optimally balances cost and performance in inference scenarios.
API Access
Has API
API Access
Has API
Integrations
Amazon EC2
Amazon EC2 G4 Instances
Amazon Web Services (AWS)
MXNet
PyTorch
Raspberry Pi OS
Slack
TensorFlow
X (Twitter)
Integrations
Amazon EC2
Amazon EC2 G4 Instances
Amazon Web Services (AWS)
MXNet
PyTorch
Raspberry Pi OS
Slack
TensorFlow
X (Twitter)
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
Actcast
Founded
2018
Country
United States
Website
actcast.io
Vendor Details
Company Name
Amazon
Founded
2006
Country
United States
Website
aws.amazon.com/machine-learning/elastic-inference/
Product Features
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization
Product Features
Infrastructure-as-a-Service (IaaS)
Analytics / Reporting
Configuration Management
Data Migration
Data Security
Load Balancing
Log Access
Network Monitoring
Performance Monitoring
SLA Monitoring