AI From The Cloud – A Look At The Practice

More and more companies are using Big Data analyses to gain insights into their strategic orientation. AI and machine learning, have not only established themselves as technologies for valuable tools but also as real game changers for almost all industries. Their potential to deliver intangible insights in real-time and convert them into recommended actions makes them a technological must-have.

No wonder because intelligent algorithms have long been part of everyday life in companies and consumers. They optimize processes, recommend suitable additions to the online shopping cart or news feed, and create the basis for self-driving vehicles for tomorrow’s mobility. All over the world, they are processing, classifying, and analyzing more data than ever before – with further increasing potential: According to forecasts by IDC and Seagate Technology, the amount of data generated worldwide is expected to increase to a total of 163 zettabytes by 2025.

Manage AI Projects In The Cloud

Projects related to AI and machine learning require trained employees and significant investments. Vast volumes of primarily unstructured data have to be processed with sophisticated mathematical models. This requires extensive, high-performance computing resources. However, many companies that want to benefit from AI cannot permanently keep the necessary resources in their servers for economic reasons or a lack of know-how among the workforce.

Sophisticated server and network technology now allow it to manage AI projects in the cloud. Dedicated resources equipped with high-performance components of the latest generation, such as the Nvidia Tesla P100 processors for parallel computing and optimized from the ground up for AI applications, as well as solutions based on containers and with cluster algorithms, enable companies a cost-efficient and at the same time highly effective use of data. Modern hybrid infrastructures are also suitable for many AI applications and enable seamless scaling if required without compromising on data protection or security.

New Business Models For The AI ​​Era

In this way, AI and cloud-based SaaS solutions create the basis for countless new business models based on real-time data analysis and process automation.

One example is the Swiss start-up Finity SA, whose Paper. Li platform revolutionizes digital marketing. Thanks to AI-supported content curation and automation, self-employed and small business owners can act as if they had an entire marketing department at their disposal. A sophisticated algorithm creates personalized content for the website, social media, and newsletter daily so that users can actively use their digital marketing channels with minimal effort. In addition, the AI ​​curates topic-related tips & tricks for users.

Finity processes 405 million social media posts in a distributed infrastructure and crawls 46 million URLs every day. The company uses machine learning to train a classification model that can be used to assign items to a predefined list of categories. Using NLP(Natural Language Processing) solutions, among other things, the keywords of each article can be extracted at high speed and used for article searches. The AI-powered classification model and NLP capabilities are critical to the platform’s capabilities to provide customers with appropriate content for their digital channels. Every day, 248 million articles, photos, and videos are published, 200,000 newsletters are sent, and 40,000 tweets are posted via Paper. Li.

Because of Paper. Li is used by customers all over the world. The company relies on infrastructure with more than 90 servers in a distributed cluster with high-throughput I/O processors and good global peering to make the platform services available everywhere without latency to be able to, In addition, the use of cloud instances in external data centers allows the infrastructure to grow with the success of the start-up. With double-digit growth rates, on-premises systems would quickly reach their physical limits.

Fully Informed, Thanks To AI

However, modern AI is also changing sectors in which the analog has set the tone, for example, in political communication (public affairs). To be fully informed about all developments in the political circus, public affairs professionals have had to tirelessly comb through dozens of different sources of information with diverse, inconsistent formats, which takes experience and costs a lot of time. Time is critical to success because thousands of papers are published at various political levels and have to be viewed and processed daily. Policy Insider. Uses artificial intelligence to close some of the typical gaps between aspiration and feasibility in political knowledge management and to offer public affairs departments relevant insights into political decisions at a glance.

The company’s expertise in public affairs is combined with the latest AI algorithms to provide public affairs professionals with accurate information promptly and thus enable well-informed decisions. The online portal scans around 5,000 new political documents and 5,000 social media profiles of political incumbents in the background every day. To bridge language barriers and offer customers uniform insights from several countries, Policy-Insider.AI uses its own AI translator, which is specially trained to translate political documents. In addition, intelligent algorithms at the provider provide additional context, for example, by showing users comparable, already known records and showing connections.

To enable such an AI-supported service, large amounts of data must be processed in real-time via complex processes. A reliable infrastructure with high-performance components is of crucial importance for this. With access to the latest GPUs, algorithms are efficiently trained and executed. Given the ever-growing amount of political documents and data sources in the digital space, platforms like Policy-Insider.AI also need the ability to scale their solutions to cover the full range of political processes. On this basis, the company trains AI models with good inference performance.

Full AI Potential – Low Administration Effort

As the examples above show, modern, optimized servers in high-availability data centers can support companies in the challenges of AI and machine learning projects. Users need sufficient capacity for AI applications with intensive workloads and the highest compliance and security standards for all connections within and between external data centers and on-premises infrastructures. Such cost-effective and easy-to-manage architectures enable more and more companies to develop and apply intelligent solutions to exploit the full potential of their data assets in the AI ​​age and thus secure competitive advantages.

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