AI is already part of the day today. This technology generates the recommendations we receive when using, for example, audiovisual platforms or when buying in online stores; Scientific bodies use it to advance their studies, and even the NBA relies on it to measure the qualities of the players offer personalized broadcasts to its fans.

Today, AI can be present in any company that wants to make the most of and maximize the value of the huge amount of data that comes from both internal and external sources. Deploying Artificial Intelligence solutions is the best way to stop seeing Big Data as a problem rather than a generator of opportunities.

Keys To Supporting AI Applications And Workloads

To implement an Artificial Intelligence project successfully, it is necessary to audit and update the IT infrastructure. Traditional DataCenters do not respond to the demands of an environment where the volume of data is continually scaling and must move agilely between systems.

AI demands data-intensive workloads that demand specific storage, networking, computing, preparation/cleaning, governance, access, and security. Thus, it is necessary to clearly define where the data will be stored, how it will be moved, how it will be processed and how it must be prepared so that it is suitable.

Optimizing corporate IT resources takes considerable effort and requires in-depth knowledge. Few companies can carry out the planning, implementation and connection of all the right systems in the minimum time and with the maximum efficiency to apply the benefits of AI in the business as soon as possible.

Partnering with experts who have the appropriate training to lead this IT modernization, such as the professionals, ensures the deployment and maintenance to support any AI initiative: Machine Learning, Deep Learning, and natural language processing.

What Storage Do I Need For Artificial Intelligence

Storage is a priority resource in any AI project. It has to guarantee its robustness in a scenario of continuous data growth and its agility to provide it to the appropriate systems. When choosing the best storage for AI, you must consider its scalable capacity, IOPS values ​​to determine latencies and throughputs, and BigData management capabilities.

The choice of storage systems will depend on how the AI ​​applications are used. In those scenarios in which it is necessary to make decisions in real-time, flash storage systems are the most demanded. But some companies choose to analyze the data after its processing without so much haste, so other options are opened, including services in the Cloud.

What is necessary is to know where the data resides and assign them to systems with one or other characteristics depending on the use that the Artificial Intelligence solutions will make of them. AI itself is one of the most advanced storage resources to distribute data in different locations based on its frequency of use and value.

What Kinds Of Networks Are Needed For AI

The quality of communications is critical to guarantee the flow of algorithms and data that feed the different Artificial Intelligence technologies. For this reason, in IT architectures focused on AI, everything that provides high bandwidth and low latency must be prioritized. And here, the networks are fundamental.

The high exchange of large volumes of data between different systems requires advanced corporate IT network design. In this scenario, it is best to opt for solutions that automate the management of IT networks. The network defined by software SDN (Software Defined Network) allows network requests to anticipate, detect threats and act in real-time.

Processing, Data Cleaning And Security

Traditional CPU-based computing is not enough to apply the various varieties of Artificial Intelligence, and it needs to be accelerated by GPUs (Graphics Processing Units). IT vendors from both fields have come together to provide AI-oriented equipment capable of processing large volumes of data in a scalable and energy-efficient way.

But no matter how much computing, storage and networks are improved, if the data you work with does not have quality, it will be impossible to benefit from AI because the results will not be reliable. Therefore, before optimizing these resources, it is mandatory to prepare the data eliminating duplications, inconsistencies, outdated, etc. For this, solutions must be implemented that automate the cleaning of data based on rules and algorithms.

At the endpoint of a plan to implement AI in the company, the safe and efficient delivery of data must always be placed. In other words, they are guaranteeing their accessibility through any device to be available whenever they are needed. The management solutions, identity and access, and encryption tools data secure protected and controlled deliveries.

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