Benefits of Integrating the Cloud and AI

There are many benefits of integrating the Cloud and AI. As for AI, it touches every industry around the globe. As part of this technology are machine learning, deep learning, computer vision, and natural language processing (NLP), which give computers faculties that mimic humans such as seeing, hearing, and even deductive reasoning.

Many enterprises need to process a tremendous amount of data efficiently, quickly, and accurately. Therefore, they depend on AI-capable colocation data centers. The need for enterprise AI applications is growing so fast that one research company predicts revenue will reach the $31 billion mark within the next seven years.

For predictive analytics programs, the top industries include education, health care, financial, and telecommunication. The goal is to target new business opportunities and improve the customer’s experience. A perfect example is a bank that uses an AI system for tracking information about credit card transactions. With pattern recognition, this bank can identify fraudulent acts.

A Unique Relationship

Cloud computing facilitates much of the progress in AI and machine learning. With massive data to analyze, Cloud computing is now more critical for delivering AI solutions. Along with prominent Cloud platforms such as Google and Microsoft, several smaller ones are integrating AI technologies.

With a unique relationship, the Cloud delivers data learned by AI systems. At the same time, AL provides information that expands the data available to the Cloud. For improving storage, computing, and other Cloud services, AI will become even more critical than it is now.

Data center colocation providers and the Cloud work like a well-oiled machine. Data center colocation services will continue to provide an infrastructure strategy for a host of companies, while AI will keep integrating with the Cloud, which will increase the need for colocation services.

Telehouse CloudLink, a connectivity exchange for customers with multiple Cloud providers, guarantees a safe and private connection between company networks and Cloud services.

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Data Center/ AI Stories You Might Have Missed Last Year

The rapid progress of artificial intelligence (AI) is impacting the global data center industry in multiple ways. Colocation service providers are looking at ways to use artificial intelligence for energy efficiency, server optimization, security, automation, and infrastructure management. As an owner of data centers in New York, Los Angeles, Paris, and other prominent global locations, Telehouse is interested in the advancement of AI in the global data center space. Here are some stories that captured our attention last year. We think these stories will have far-reaching impact.

Data Centers Get AI Hardware Upgrade from Big Hardware Manufacturers

The hardware market for AI-based applications is heating up. Intel, AMD, Microsoft, Google, ARM, and NVIDIA have announced their own specialized hardware targeted at artificial intelligence. Intel unveiled its Nervana Neural Network Processor (NNP) family of chips specifically designed for AI applications in data centers. AMD’s EPYC processor with 32 “Zen” cores, 8 memory channels, and 128 lanes of high-bandwidth I/O is also designed for high-performance computing. Microsoft is experimenting with Altera FPGA chips on their Azure Cloud to handle more AI processing.

Google’s announcement of Tensor Processing Unit (TPU) on the Google Cloud Platform probably received the most press. TPU is optimized for TensorFlow, the open-source application for machine learning. NVIDIA’s graphics cards are already in big demand for machine learning applications. But it has unveiled the Volta GPU architecture for its data center customers.

ARM processors are generally known for their use in low-power mobile devices. But it is taking a stab at the Data Center AI market with two new offerings: Cortex A-75 and Cortex A-55.

With the big names in the hardware industry fighting for dominance, global data centers will have a plethora of hardware choices for AI applications.

Personal Assistants Are Driving the Demand for AI Processing

Amazon Alexa, Google Assistant, Apple Siri and Microsoft Cortana are competing with each other to gain the next-generation of users. As more people start using voice queries and personal assistants, it is changing the dynamics of internet search. The change is significant enough to threaten Google’s dominance. If future users move to voice for daily searches, Google has to rethink their advertising strategy. The winner of the personal assistant battle can end up owning the future of e-commerce.

Artificial intelligence is the backbone of the personal assistant technology. According to a Consumer Intelligence Research Partners (CIRP) survey, Amazon has sold more than 10 million Alexa devices since 2014. Because the personal assistant market is lucrative, innovative startups will try to disrupt the space. And these newcomers will require massive data centers to handle their AI processing needs. As the number of related devices and applications proliferate, the need for global data centers with AI capabilities will also increase.

Big Basin and Facebook

Facebook’s do-it-yourself (DIY) approach to AI hardware might become the model for colocation service providers. Facebook uses artificial intelligence for speech, photo, and video recognition. It also uses AI for feed updates and text translations. So they need hardware that can keep up with their increasing AI requirements.

Big Sur GPU server was Facebook’s first generation AI-specific custom hardware. It was a 4U chassis with eight NVIDIA M40 GPUs and two CPUs with SSD storage. Facebook learned from their experimentation with this hardware configuration. They took that learning and used it to build the next-generation Big Basin architecture. It incorporates eight NVIDIA Tesla P100 GPU accelerator and improves on the Big Sur design. The added hardware and more modular design have given Big Basin a performance boost. Instead of 7 teraflops of single-precision floating-point arithmetic per GPU in Big Sur, the new architecture gets 10.6 teraflops per GPU. Continue reading.

Contact Details:
Telehouse America
7 Teleport Drive,
Staten Island,
New York, USA 10311
Phone No: 718–355–2500
Email: gregory.grant@telehouse.com

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Algorithms: Smart Yet Slightly Frightening

In smart cities, as well as data center and colocation facilities, algorithms play a critical role. Algorithms are the reason computer operating systems exist and, therefore, the World Wide Web and Google. For a colocation provider, algorithms make it possible to provide customers a safe and reliable service.

Algorithms also help transform Big Data, initially converting it into analytics and then into an action. Colocation service providers are at the heart of smart cities, with algorithms assisting Data Center Infrastructure Management (or DCIM) tools in predicting cooling problems.

Load balancing algorithms are critical for colocation services, distributing application or network traffic across servers, thereby making them more efficient. There are also smart storage algorithms that process rich media requests, including videos, and cut energy consumption for enterprise-level storage area networks by as much as 50 percent.

In unimaginable ways, algorithms impact both personal and professional lives, which is exciting, yet somewhat unnerving. As an increasing number of businesses adopt and enhance digital solutions, there is a strong chance of seeing more colocation service providers relying on algorithms for storage, computing, and networking.

For organizations with business-critical data, Telehouse provides superior colocation services with 48 data centers worldwide. Ultimately, business owners have peace of mind thanks to high security, redundant power, and flawless interconnection to virtually hundreds of service providers.

Contact Details:
Telehouse America
7 Teleport Drive,
Staten Island,
New York, USA 10311
Phone No: 718–355–2500
Email: gregory.grant@telehouse.com

Read More