As discussed in previous blogs, AI is the broader concept that aims to create systems that can perform tasks akin to humans. Machine learning is a way to learn from and make informed predictions and decisions based on data. It can be thought of as yet another form of automation that involves using algorithms to learn and improve over time without explicit programming. Finally, Data Science, as a multidisciplinary field, melds techniques from statistics, mathematics, and computer science to enact a wide range of activities, from data analysis and interpretation to the application of machine learning algorithms. Thinking about it broadly, we could divide applications for AI, ML, and data science into two broad categories: Predictive AI and Generative AI. Predictive AI aims at predicting and analyzing existing patterns or outcomes (e.g., classification, clustering, regression, object detection,etc.). In contrast, generative AI aims at generating new and original content (e.g., LLMs, RAG17,etc.). As such, the algorithms and techniques underpinning predictive and generative AI can vary widely.
Illustration – Cloud Native AI Stack (credit CNCF)
This cloud-native AI stack layer consists of software technologies for building AI enabled cloud-native applications. For example, AI developers use cloud technologies like infrastructure such as AWS, database, messaging, container images, and continuous integration (CI) and continuous delivery (CD) tools to build cloud applications. The next few blogs will discuss different layers of this stack.
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