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This is where I write about AI and Data Science. As I work as a data scientist/machine learning engineer, I read a lot of books on machine learning and AI and try to develop my skills and knowledge on the topic. I tried to dangle a bit into AI Strategy and discuss what I have learned and what I thought was a good way of approaching AI as a strategic asset and opportunity.

Data Science

I think the notes on the Data Science Project Start-Up Phase are cool; I have worked a lot on the data science aspect of projects and have learned some hard-learned lessons, and I think a lot of those will be of interest. I really thought the Confident Learning topic was of interest, and I recommend going there to get more knowledge about that topic.


I wrote a lot about LLMs in 2023, and some of this might be a bit outdated, but I still think that it is very much a good starting point for LLMs.



  • Even though machine learning LLMs such as GPT-3, GPT-4, and LLama are non-deterministic. There are a few parameters that determine the output; there are few outputs that determine the inconsistence
    • Seed - a random number that the model uses to start calculations.
    • System Fingerprint - describes the state of the engine.
    • temperature / top_p - how the model samples based on log probability