diff --git a/content/about/index.md b/content/about/index.md index 3e467dc7e..8ba385b4f 100644 --- a/content/about/index.md +++ b/content/about/index.md @@ -59,6 +59,6 @@ Let's go deeper with a few highlights from my work: One of the downsides of working in an ever-changing field and accumulating a broad range of experiences is that it's hard to summarise with a concise title. For example, being a data scientist [used to imply having strong software engineering skills](https://yanirseroussi.com/2014/10/23/what-is-data-science/), but [this has changed over time](https://yanirseroussi.com/2023/06/30/was-data-science-a-failure-mode-of-software-engineering/). It's a similar story with the decline and rise of artificial intelligence. In an ideal world, I'd be able to let my work speak for itself. In our world, people search for keywords and have different understandings of concepts, e.g., they may want "an AI solution" to a problem that can be solved with deterministic software engineering. Or they may believe they need an AI Engineer rather than a Data Scientist, when a few years ago it'd have been the opposite (as I'm writing this in 2023, you could replace the words _data science_ with _artificial intelligence_ across my historical posts and much of what I wrote would still hold). -Anyway, whether you're trying to navigate Data & AI terminology or solve specific problems, I can probably help. My aim is to get to the root of business problems and iteratively implement pragmatic solutions. The taxonomy of Data & AI professionals is only relevant if I'm helping you hire a team. +Anyway, whether you're trying to navigate Data & AI terminology or solve specific problems, I can probably help. My aim is to get to the root of business problems and iteratively implement pragmatic solutions. The taxonomy of Data & AI professionals is only relevant if [I'm helping you hire a team](https://yanirseroussi.com/tags/data-hiring/). {{< figure src="mlops-roles.png" width="500" height="433" caption="A subset of roles I've performed in one way or another.
Source: [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://ieeexplore.ieee.org/document/10081336)." alt="Venn diagram showing different MLOps-related roles: Data Scientist, ML/MLOps Engineer, Backend Engineer, Data Engineer, DevOps Engineer, and Software Engineer" >}}