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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Without An Effective Big Data Strategy There Is Neither Analytics Nor Artificial Intelligence

The upgrade of the IT infrastructure is needed to access structured data and unstructured data. It is no longer enough to consult the data of corporate business solutions, and now it is necessary to incorporate the flow from emails, photos or videos to chat conversations, social media publications. The problem is that this new scenario demands greater capacities in three essential IT resources: storage, computing and network.

Only with the modernization of the data center focused on Big Data will it allow decision-makers to have a wide range of data to combine in their analytical solutions to get the most right. Moreover, without an IT restructuring, it will be impossible to apply Artificial Intelligence and easily access automated proposals with which to make decisions in real-time that allow everything from customer loyalty or discover new business opportunities, to avoid risks or optimize processes.

It is necessary to have an agile, flexible and scalable IT architecture to avoid bottlenecks in the delivery of the information requested by users, and at the same time capable of adapting to changing business requirements, aligning with market trends. All this without incurring in continuous and excessive costs.

Choosing The Best It Resources For Big Data

Mass data processing demand robust and efficient systems. IT vendors offer multiple valid options, but their choice is difficult without an internal technology team with sufficient knowledge and expertise. Trusting the process of consulting and deployment in Orbit Consulting Group simplifies this task and allows any company to have a Big Data & Analytics successful strategy.

Many organizations rely on IT infrastructure services in public Cloud to establish their Big Data IT architecture, taking advantage of its ease of scaling and avoiding large investments in on-premises solutions and complexities in deployment. But directing Big Data only to the Cloud is not a magic solution to meet your needs. The cloud environment has its limitations, as discussed below.

Public Cloud Issues For Big Data

The first stumbling block is that 100% compliance with all data privacy and confidentiality requirements required by both sectoral and general GDPR- type regulations is practically impossible in public clouds. Suppliers are the ones who set the rules, and it is difficult for them to fully adjust to the particularities of each company (SLAs, SLOs.).

On the other hand, the search for specific plans that adapt to existing budgets and the changing needs of the business and departments leads to the contracting of multiple cloud services from different providers. They are multi-cloud environments. But managing multi-cloud infrastructures is quite complex. Harmonizing each of the services so that corporate IT acts as a whole, securing it and, at the same time, containing costs so that they do not skyrocket difficult.

Deploy Hybrid Environments For Big Data And Analytics

This scenario is causing the majority to opt for Big Data strategies in hybrid IT environments. The combination of private Cloud and public Cloud makes it possible to rationalize the use of resources based on their criticality and the type of data being processed, placing in physical those instances of greater value that require greater protection, and overflowing in the public Cloud what is needed as a compliment.

Nobody is aware that managing and monitoring the Big Data infrastructure requires enough knowledge and experience in IT systems to balance teams according to workloads, seeking a continuous and agile flow between resources on-premises and in the Cloud. 

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