Why Open Source Platforms Are Reliable for Enterprise Artificial Intelligence

Open source is parallel with Artificial Intelligence and is largely responsible for the instant growth of enterprises in the past few years. AI-powered development platforms such as TensorFlow, Keras, and CNTK are all open-source frameworks with their code posted on GitHub, which professional developers can enhance and build different versions if needed. Numerous frameworks have private backers. These are all largely driven by a growing ecosystem of third-party developers. The productive nature of developing AI algorithms and models using the frameworks, with the research openly published on arXiv. The code downloadable on GitHub for productivity has remarkably fueled the AI revolution, somewhat unique to other technology revolutions in the past.

Expanding Model and Data Repositories to Indulge Deep Learning

The open-source nature of Artificial Intelligence collaboration only goes so far within the enterprise. Boosting AI model development on open-source frameworks is just a single part of the process. Besides model building, enterprise AI development needs to have strong model and data repositories that can be easily onboard, with the capability to deploy models in various target environments and extra capabilities that focus on boosting and sharing models.

Enterprise Artificial Intelligence platforms have been largely proprietary in nature. Most of the enterprise AI-based platforms have been entirely focused on conventional machine learning capabilities which is also known as data science or data analytics. These include a wide range of platform players like IBM, Microsoft, Dataiku, SAP, and others.

Extra support for deep learning models is not that crucial in enterprise data science platforms, with some vendors being ahead of the others. This is a spot where the problem lies. For the major part, data analytics tools that are quite common across several enterprise segments have been slow to join the deep learning revolution. Some of the most common open-source frameworks are largely deep learning frameworks, which is a special branch of machine learning that uses deep neural networks such as convolution neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, or Q-learning.

Deep learning models are best suited for perceptual issues such as vision or language. But their applications are protruding into broader data classification or predictive analytics where conventional machine learning algorithms have been used. Reinforcement learning is a completely different branch of machine learning, which has major potential for enterprise applications and is also largely supported on open-source frameworks instead of other enterprise AI platforms.

Matching the Platform Development Capabilities of Hyperscalers

Hyperscalers such as Google, Facebook, and Microsoft have already developed their own internal platforms to super-scale deep learning models and used them within their own companies. For instance, Google has been able to assist a company in saving energy across the major data centers. They end up incorporating their own model development frameworks but then complement that with other pieces to offer a bigger enterprise-scale framework to scale AI within their organization. What is needed in the wider enterprise context is an open-source platform that offers similar capabilities? Also, the technology gap that is emerging between hyperscalers and other enterprise companies in terms of AI adoption, most of the enterprise data science platforms also limit users from exporting models, constraining their usage and scalability.

Acumos Is a Reliable Tool for Determining AI Application Development

Acumos was successfully started by the Linux Foundation as an open-source telecom Artificial Intelligence platform. However, it has now developed into a more general platform for constructing AI-powered applications across the enterprise. Acumos AI is an open-source framework that can streamline the development of AI-based applications with the help of wrapping tools. Professional developers can export libraries, models, and all other required information under the name of Docker files.

Acumos has recently accumulated widespread support. The AI platform has seen a wide range of use cases starting from 5G to media and entertainment, security, automotive, mobile, infrastructure, manufacturing. The instant growth of use cases may not be surprising, as the AI developer community and the wider enterprise ecosystem requires an open-source platform such as Acumos to boost enterprise AI adoption. Additional details about the growing membership and use cases can be expected to be announced in the following months.

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