Artificial Intelligence (AI) is one of the best human inventions and plays a crucial role in our daily life. We use many AI applications in many streams such as Commerce, Health Management, Retail, Intelligent Cybersecurity, to improve workspace communication, Chatbots, and many more. But, the most challenging task is to deploy these AI applications and to develop AI models as well. ACUMOS AI is the best solution to empower AI growth. In this article, we are going to all about ACUMOS AI.

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What is ACUMOS AI?

ACUMOS AI is an open-source platform that helps to build, deploy, and share AI applications easily. It was co-developed by Tech Mahindra and AT&T and hosted by Linux Deep Learning Foundation. ACUMOS offers a large ecosystem that is used to build AI models of Machine learning and Deep learning very effectively. This tool helps developers and model trainers to focus on core and accelerates innovation. 

Features of ACUMOS AI

The ACUMOS AI is a complete ecosystem for the lifecycle of Machine learning and Artificial Intelligence. The best features of ACUMOS AI are as follows:

  • ACUMOS AI allows API to connect with toolkits and models as Microservices. 

  • It exports AI apps in the cloud environment as Docker images.

  • The Design Studio tool of ACUMOS AI is used to develop visual programming code for AI applications.

  • It provides easy deployment and development with the Dockerization of AI applications.

  • Enhanced user experience in the portal by model building, publishing, onboarding, deploying, etc.

  • It offers onboarding ramps for ML models and AI toolkits.

  • It categorizes the difference over Deep Learning / Machine Learning libraries enclosed by common API.

  • With ACUMOS, it is easy to add models and toolkits.

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    ACUMOS AI allows sharing models to improve application creation.

  • With visual IDE, models and toolkits can be created easily, and no coding skills are required.

ACUMOS AI Framework

The framework of ACUMOS AI are as follows:

Athena

Athena is the first release of the ACUMOS AI platform in 2018. It provides the capability to deploy models into cloud infrastructure or in the Kubernetes environment on users' own hardware, including virtual machines and servers. With Athena, bugs and issues can be detected and eliminate while developing AI models. 

Boreas

Boreas is the second release of the ACUMOS AI platform in 2019. It makes significant changes in all components and supports for onboarding Dockerized, PFA, and ONNX models. Boreas supports an enhanced system peering through a controlled process of partner catalog subscription and publication. 

Clio 

Clio is the third release of the ACUMOS AI platform which is also in 2019. It includes new features and fixes all the bugs of the components. Clio has a great feature that helps developers to create, integrate, and deploy NiFi data pipelines. It allows ONAP integration for virtualized network elements optimization and automation.

Demeter

Demeter is the next framework release of the ACUMOS AI in 2020. It is a containerized platform deployment, that includes cloud-native functions, implementing flexibility, and scaling horizontally. Demeter supports Dockerized and Pre-dockerized models with files to render models in the Design studio.

Elpis

Elpis is the upcoming release of the ACUMOS AI platform and expected to release soon. 

ACUMOS AI Architecture

ACUMOS AI is the best structure that helps to build innovative AI models with its integrated solutions. It creates an open-source platform for deploying, licensing, packaging, and sharing AI models as a microservices, portable and publishes them in a secured catalog.

ACUMOS AI architecture includes five significant modules that play a crucial role in the process of AI development. Let’s check those five modules in detail.

Onboarding

The main aim of onboarding is to provide an interface through web or Command-line interface (CLI) for various types of models. It helps to create the required identifiers and artefacts in the ACUMOS platform. Onboarding models existing in four onboarding languages of ACUMOS clients such as R, C++, Java, and Python. Onboard Portable Format Analytics (PFA) and Open Neural Network eXchange (ONNX) libraries are used to deploy and enhance importing and exporting Deep learning models from various AI framework.

Design Studio

Design Studio is a component repository that includes the composition engine, Generic Data Mapper Service, SQL Data Broker, TOSCA Model Generator Client, and CSV Data Broker. Simply, it is a graphical tool in ACUMOS designed for filtering, chaining, and models that can be used to solve different data problems.

SDN and ONAP

Software-defined networking (SDN) provides a global network controller that manages, assigns, and provisions of network resources. It supports the ability to invoke other local SDN controllers, including third-party SDN controllers. 

Open Networking Automation Platform (ONAP) aims to provide performance, Security test for ONAP components, system-level, module, Scalability, and functions. It develops auto test environment and auto test scripts or use cases.

Team UP

In this module, ACUMOS provides an open-source ecosystem for people to work, collaborate, and share solutions and ideas to provide better outputs.

Marketplace

ACUMOS provides a marketplace and acts as a go-to-site for making data-powered decisions. It even makes AI as an easy-to-use initiative in Marketplace and Design studio. 

Steps to create AI models in ACUMOS AI

The AI development process on the ACUMOS AI platform includes four significant steps. They are as follows:

Create and Onboard models

ACUMOS AI enhances the development, training, and deployment of AI models. It includes AI development tools and services such as SciKit-Learn, Tensor flow, RCloud, H2O, and several programming languages. Each tool implements around a specific set of libraries. It makes each toolkit and languages interoperable with each other, compatible solutions that ACUMOS can use and will grow over time.

Execute in Target Environment

In this step, the deployment process takes place. ACUMOS package solutions into Docker image files can be deployed into the Docker environment and managed through a set of container tools such as Kubernetes. ACUMOS Platform offers tools to package any set of components including adapters, predictors, and other microservices to create a compatible, deployable image file in any runtime environment.

Enhancing models with application datasets

The next step is to provide a training and testing interface that is required to turn a basic model into a predictor to perform specific functions. ACUMOS takes the packaged models to a secure runtime and Training lifecycle without changing the model. This step can be done by using custom training clients, data caching tools, and data access to make it easy for assembling the training application of each model. 

Sharing models in Marketplace

ACUMOS creates a catalog of useful functionality that connects a component developer that performs searching, reviewing, chaining, and rating of machine learning models. ACUMOS provides a design studio that makes easy to chain a series of components by connecting data sources to model-based predictors to operate functions and efficient decision tree. It makes it extremely easy to integrate independent and well-defined microservices into powerful IT Solutions. 

These four steps play a crucial role in the process of AI model development that is supported by ACUMOS.

Languages supported by ACUMOS AI

ACUMOS AI platform supports integration with several languages to make deployment of AI models much easier. It supported languages are R, C++, Java, and Python. The supported languages might extend further that increases the level of developing the AI models. 

Impact of ACUMOS in AI environment

Usually, AI applications are build by various popular AI frameworks. But, the process of integration is a challenging task for the beginners as they are based on the cloud environment. ACUMOS AI can do the integration process with Design Studio based on Linux and provides an easy way to deploy AI models. This way, ACUMOS AI will empower the field of Artificial Intelligence.

Conclusion

The main aim of ACUMOS is to make AI and machine learning accessible to a wide audience by creating an extensible marketplace of reusable solutions, sources from several toolkits and languages. It makes an ordinary developer who is not an expert in this field can easily able to create their models and applications. Such an AI platform is required to meet the demands of business use cases, including field services, network analytics, health care analysis, network security, election results auditing, advanced video services, and customer care services. As an open-source, developers can create solutions to real business problems more quickly, resulting in substantial business values. 

In this article, we have learned What ACUMOS AI, features, frameworks, and architecture of ACUMOS AI, and the process of development of AI models is. I hope you have useful information.