In today’s innovation realm, Edge AI workflow management platforms are becoming a powerful toolkit for AI developers, companies in the AI space & hardware manufacturers to handle their complex development.
With an effective end-to-end management platform, AI developers are able to FastTrack their projects, make their AI models sharper and save development costs by a considerable amount enabling them to focus better on the product.
However, choosing the right workflow management platform is very important to support the development cycle, technical configurations, resource abilities and align the business goals with outcome to meet the expected results.
That is why we at EDGENeural.ai devised a platform that tackles the most commonly faced snags by AI developers & companies while adopting AI on Edge.
Introducing ‘ENAP Studio Beta: Early Access’
ENAP Studio is a modular, unified, hardware-agnostic Edge AI acceleration platform that enables AI developers to train, optimize, compile and deploy deep learning models at blazing-fast inference speeds across commonly used hardware.
In a nutshell, ENAP Studio democratizes AI and makes AI on Edge adoption faster, better, effective and effortless!
Let’s take a look at some of the key features in detail:
1) Train AI Models Easily
ENAP Studio provides an easy, UI based tool that can train your AI Models from scratch, use already built models or select a pre-optimized model from the repository. Using our easy-to-use training module, one can easily add a link to the dataset and start the process within a matter of few minutes. In addition to this, users can graphically view the progress of training for every epoch with accuracy and loss functions along with detailed reports
Our training module presently supports a wide range of computer vision models including detection and classification. Different model families supported presently are:
- Classification – ResNet, VggNet, DenseNet, SqueezeNet, AlexNet, MobileNet
- Detection – YOLO, SSD
We will soon be adding models for segmentation and other tasks.
2) Optimize AI Models Efficiently
ENAP Studio assists in optimizing the inference performance of AI Models without trading off the accuracy and helps in achieving better outcomes without any heavy-lifting to be done manually. Using our AI-powered platform, one can easily optimize their trained AI Models with a few clicks by selecting the model, choosing hardware & quantization levels.
ENAP optimizer automatically crafts a robust model by various quantizing and pruning techniques making it lightweight and seamless to deploy on resource-constrained hardware.
3) Enables Continuous Deployment Seamlessly
ENAP Studio lets you simplify your deployment across different hardware. With our inference engine, one can easily deploy optimized AI Models onto a wide range of hardware without any additional efforts and save hours of engineering time. Additionally, the inference engine with an AI model can be easily integrated into the application with few lines of code.
ENAP Studio lets you scale your inference workloads, build, and optimize computer vision models on Edge devices including Nvidia and x86 GPU. We have more hardware support planned and these will be added soon. If you would like to add your choice of hardware to the ENAP Studio do mail us at firstname.lastname@example.org.
4) Ready-To-Deploy Models With Model Zoo
ENAP Studio comes with a repository of State-of-the-art(SOTA) pre-trained and pre-optimized models that can be used for any compatible Edge hardware. This plug and play feature helps AI developers easily pick the desired models, drag and drop them to build Edge AI Models for their hardware.
We are also continuously adding new models to our platform which can enable more functions and speed up the development processes while assisting developers to build next-generation cutting edge applications on edge devices using ENAP Studio
5) Facilitate MLOps / Edge DevOps out of the box
ENAP Studio comes with MLOps features including model versioning to easily manage multiple development pipelines across various applications.
This out-of-the-box feature of ENAP Studio provides a single pane to manage development assets, and production deployment abilities well-organized and function-crafted.
In addition to these features, ENAP Studio lets developers, managers and companies incorporate the best practices thereby increasing the quality, simplifying the management process, and deploying machine learning and deep learning models in production environments.
ENAP Studio can be presently used for computer vision use cases of classification and detection like surveillance, face detection, retail use cases or manufacturing industry use cases like defect detection, etc.
We at EDGENeural.ai are building and continuously evolving a scalable, result-oriented, software-defined platform for AI Developers that will revolutionize the way AI Adoption is foreseen in the industry. ENAP Studio Beta is our first step towards this vision and goal.
We are very excited to take ‘ENAP Studio Beta’ live and assist AI developers to build new and profound innovations that could uplift the world.