Use cases

0:00 for many years data science has been called the sexiest job of the 21st century but in recent years it seems like there's a new job buying for that title the AI engineer so who even are these New Kids on the Block are they just data scientists in Disguise what's up y'all I'm Isaac key and I'm a former data scientist turn AI engineer at IBM to answer these questions I'm going to lay out four key areas in which the work of a data scientist differs from an AI engineer specifically a generative AI engineer but before before I dive into these differences we first have to understand more about what's happening in the industry so traditionally data scientists have always used AI models to do their analysis so what's changed well with the Advent of generative AI the boundaries of what AI can do are being pushed in ways that we've never seen before and so these breakthroughs have been so groundbreaking that generative AI has split off into its own distinct field and we call that AI engineering Okay so now that we understand the landscape let's dive into the differences the first area of difference lies in the use cases so at a very high level think of a

1:09 data scientist as a data Storyteller they take massive amounts of messy real world data and they use mathematical models to translate this data into insights on the other hand think of an AI engineer as an AI system builder they use Foundation models to build generative AI systems that help to transform business process so since data scientists are fantastic storytellers they use a lot of descriptive analytics to describe the past one example of this is through what's called exploratory data analysis or Eda which is all about graphing the data and doing statistical inference they can also do this through what's called clustering which group similar data points based off of similar characteristics such as say doing customer segmentation now every good story has the reader trying to figure out what's going to come next and that's where predictive use cases comes in as opposed to a book however a data scientist does not have the end already written so they have to use what are called machine learning models to to make their predictions an example of this is called regression

2:20 models which predict a numeric value such as say a temperature or Revenue another type of these models are classification models which predict a categorical value such as a success or a failure so putting on the AI engineering hat now one of the main use cases that AI Engineers work on are called prescriptive use cases which are all about uh choosing the best course of action an example of this is a technique called decision optimization which enables businesses to assess a set of possible actions and then choose the most optimal path based off a set of requir requirements or standards another example of a prescriptive use case is through uh creating what are called recommendation engines uh as an example this can involve suggesting uh targeted marketing campaigns for a select customer base in addition to prescriptive use cases there are also generative use cases hence the name generative AI now Foundation models which why I will touch on more in a bit enable the creation of what are called intell

3:32 assistants uh for example a coding assistant or a digital adviser they also enable the creation of chat Bots as an example which enable conversational search through information retrieval and the summarization of various content so after we have a use case identified we need data now people say that data is a new oil because like oil you have to search