Chief Information Officers and other information technology leaders realize the importance of maintaining their information technology infrastructure capabilities in line with technological advances so their customers can get the services and performance they expect to keep their business growing. Large or small, all businesses have been impacted by the increasing availability of artificial intelligence (AI) systems. Whether it’s advanced analytics or machine learning, traditional IT infrastructure configurations don’t necessarily deliver the best performance for these applications. Artificial intelligence applications and services are notorious for consuming large amounts of data and requiring fast networks and storage with low I/O latency. A good system engineer will realize the key infrastructure attributes needed for high-performance AI solutions and incorporate those in its systems design.
Outsourcing your infrastructure needs
Some vendors such as Microsoft Cloud Services can scale as needed to meet most business requirements. This is an advantage for small business and start-ups who don’t want or don’t have the money to spend on capital expenses. It’s also useful for an established business with significant investments in IT who may be going through a growth period and need IT services quickly or want to support hybrid systems as a way to increase resilience and be more flexible in serving their IT customers.
Other cloud platforms such as Google Cloud and Amazon Web Services are also available and competing with each other to offer AI services. Their models have been designed to provide a basic framework upon which a particular business with their own needs can generate custom models by integrating several tools such natural language processing or image recognition.
Going the in-house way for IT resources
Cloud platforms and services are not the only way to obtain the infrastructure resources needed to support AI services. For multiple reasons such as security or performance, many businesses will choose to design and build what they need. To keep budgets within reason, system engineers are choosing commodity hardware when developing systems to manage the large volumes of data required by AI. Infrastructures in support of big data needs require servers, networks, and storage that are fast and reliable to collect, distribute, store, and analyse the terabytes of data throughout the enterprise.
The IT system loads and resource consumption when running AI models and its applications will vary with demand and analytical complexity. Deep learning, reasoning, problem-solving, and learning models require large volumes of structured and unstructured data formats that must be compiled and analysed in a reasonable amount of time to extract and present information for use by decision makers across an enterprise.
Investments are a subtle way to understand how serious a company is about using AI. IT infrastructure needs should not be a barrier to exploring and taking advantage of machine learning or deep learning algorithms applied to your big data inventory in order to extract the kind of information and insights that business leaders in the 21st century need to stay ahead and grow in an increasingly global and competitive landscape. From private industries to government agencies, there is no shortage of interest in AI and big data.