Introduction

In the previous chapter, we covered the fundamentals of data storage. In this chapter, we'll pe a little deeper into the architecture of Artificial Intelligence (AI) solutions, starting with the requirements that define them. This chapter will be a mixture of theoretical content and hands-on exercises, with real-life examples where AI is actively used.

Let's say you are a solution architect involved in the design of a new data lake. There are a lot of technology choices to be made that would have an impact on the people involved and on the long-term operations of the organization. It is great to have a set of requirements at the start of the project that each decision could be based on. Storing data essentially means writing data to disk or memory so that it is safe, secure, findable, and retrievable. There are many ways to store data: on-premise, in the cloud, on disk, in a database, in memory, and so on. Each way fulfills a set of requirements to a greater or lesser extent. Therefore, always think about your requirements before choosing a technology or launching an AI project.

When designing a solution for AI systems, it's important to start with the requirements for data storage. The storage solution (infrastructure and software) is determined by the type of data you want to store and the types of analysis you want to perform. AI-powered solutions usually require high scalability, big data stores, and high-performance access.

IT solutions tend to be either data-intensive or compute-intensive. Data-intensive solutions are "big data" systems that store large amounts of data in a distributed form but require relatively little processing power. An example of a data-intensive system is an online video website that just shows videos, but where no intelligent algorithms are being run to classify them or offer any suggestions about what to watch next to its users. Compute-intensive solutions can have smaller datasets but demand many computing resources from the hardware; for example, language translation software that is continuously being trained with neural networks.

AI projects are not your typical IT projects; they are both data-intensive and compute-intensive. Data scientists need to have access to huge amounts of data to build and train their models. Once trained, the models need to be served in production and fed through a data pipeline. It's possible that these models get their features from a data store that holds the customer data in a type of cache for quick access. Another possibility is that data is continuously loaded from source systems so that it can be stored in a historical overview and queried by real-time dashboards that contain predictive models or other forms of intensive data usage. For example, a retail organization might want to predict trends in their product sales based on previous years. This kind of data cannot be retrieved from the source systems directly since they only keep track of the current state. For each of these scenarios, a combination of data stores must be deployed, filled, and maintained in order to fulfill the business requirements.

Let's have a look at the requirements that need to be evaluated for an AI project. We'll start with a brief list and do a deep pe later in the chapter.