Acumos the New Power for AI

The last few years have marked a significant shift in our lives by the advancement of Artificial Intelligence. It has forged its way from a science fiction fantasy to the reality of our lives. Today, we utilize AI technologies for interacting with our cell phones, using devices like Alexa, Siri, etc.

The AI is consistently growing to impact the lives of future generations. For instance, cars today are operated by deploying automation in driving and detecting the surroundings.

The AI is fostering today only under the technical giants like Google, IBM, Microsoft, Apple, and Facebook as it is a very sophisticated project to develop AI-driven technologies. For example, there are very few or no big players in the field of networking and communication

The development of AI technology continues to remain difficult today. The significant growth is seen in the image and processing area, where the research began many years ago. The only open-source solution for this problem is ACUMOS. It helps in building precise AI applications.

The Linux Deep learning foundation hosts the ACUMOS. It is an open-source AI platform that is co-developed with the help of AT&T and Tech Mahindra, hosted by Linux Deep Learning. ACUMOS is a breakthrough invention for making AI applications. It aids in developing AI technologies efficiently.

There are already so many frameworks in the market for Artificial Intelligence. The problem with these frameworks is they are very typical and challenging; a beginner struggles to deal with them. As it includes the cloud-based environment, just advanced engineers can handle them.

ACUMOS AI comprises Design Studio established on Linux, which assists in integrating these frameworks, and it furnishes a simple form of deployment for beginners.

ACUMOS drives the data scientists by delivering custom integrated solutions complemented with creative AI models. The models under it are built using other collaborative and supportive languages like Java, Python, and R can also be formulated, deployed, and documented.

The framework is fully interoperable with other models of ACUMOS AI built apart from other AI frameworks. ACUMOS includes five significant modules that have a crucial role in building its environment. These modules support facilitating the AI development process in the ACUMOS ecosystem.

TEAM UP - This module, ACUMOS, empowers an open-source ecosystem for people to work together, experiment, and share their thoughts and solutions to generate better outcomes.

Design Studio - This tool is used for graphical means; it's mainly formulated for chaining, filters, multiple models, and many more features together into a single solution, like a run-time environment. You can use Design studio in various settings for solving different data sources and issues.

Marketplace - Marketplace is in the mainstream of AI using ACUMOS. Plus, it also functions as go to go site for generating data-powered decisions. In addition, ACUMOS makes it easy to utilize initiative in design studios and marketplaces.

Onboarding - ACUMOS focuses on interoperability, which helps it in providing enhanced support in diverse AI toolkits. Many onboarding tools are accessible in the market like H2O, TensorFlow, generic Java, RCloud, and many more; these tools help in better functioning.

SDN and ONAP - This is deployed as a community for many market place solutions. It can also be directly used in SDC.

The four main steps that are involved during AI development on ACUMOS are as follows -

1. Onboarding and building Artificial Intelligence applications - This step involves building AI applications with an API. Applications are trained and instructed by several kinds of Machine Learning/Deep learning libraries such as TensorFlow, SciKit-Learn, RCloud, and H20.

2. Register and Dockerize the application to ACUMOS AI -
Dockerization deploys environment variables and configuration files to build an environment-friendly application.
Dockerizing an application is interpreted as altering an application to run, debug, and test within a Docker Container. The same process is also implicated in the ACUMOS platform to Register AI apps into the ACUMOS environment.

3. Sharing and integrating knowledge - There are many prominent frameworks available in the market, such as TensorFlow, SciKit-Learn, RCloud, and H20. These frameworks build the AI applications very effectively. The problem is the integration of these frameworks with each other individually is not possible.

ACUMOS solved this problem by providing a platform for integration and sharing knowledge among several AI applications. Thus, several AI applications can transfer knowledge to produce better outputs.

4. Share AI applications into the marketplace - Install created AI apps in the Marketplace, comprising public and private modules. It furthermore has infrastructure engineers to retain the deployed AI applications.

ACUMOS classifies the differences between machine learning or deep learning libraries. A familiar Application programming interface then encloses these.

Dockerization provides an easy way of developing and deploying AI applications.

Design studio that is ACUMOS GUI tool. It is utilized to develop visual programming code for AI applications.

ACUMOS provides a better ecosystem for Artificial Intelligence.

ACUMOS delivers a marketplace for sharing, rating, and collaborative intelligence with Artificial Intelligence prototypes in public and private areas.

It also provides API connect, toolkits as microservices, and chain models.

ACUMOS offers a unique option to export AI applications in Docker images to run in private and cloud settings.

You can easily add models with the help of awesome tool kits on the ACUMOS platform.

It generates an onboarding ramp for AI toolkits and ML models.

Conclusion

Artificial Intelligence is going to change the workflow of the systems around us through its breakthrough solutions. Also, there is a bundle of limitations that are preventing the AI revolution. But ACUMOS established on Linux doesn’t let this happen because it facilitates stimulation of the AI innovation room by integrating with various popular AI frameworks (TensorFlow, SciKit Learn, iCloud, and H20) with each other.

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