For consumers of advanced technical products, finding and understanding product documentation and learning about the systems can be a daunting task. With a wide range of products and complex engineering knowledge required, both our internal teams and customers often struggle to get the information they need. To address this challenge, we are developing an advanced AI chatbot designed to simplify access to documentation and provide quick answers to technical questions. This solution aims to enhance both internal efficiency and customer satisfaction. Over the past seven months, significant progress has been made, and as the AI architect, I am excited to share the results and insights gained from this project. ...

One of the reasons for me to starting this blog is to start prioritizing time for myself, to develop myself, become more visible and cut out time to dive into things I am passionate about. A month of not taking any time for myself later - I must confess this is sadly not so easy. Work has an endless appetite. It feels like the more effort I put into it, the larger appetite it get. So if I keep feeding my projects at work over personal ambitions, I will never get any nurturing meals for myself. I need play! - for me play is essential. Play is a relaxed focus and curiosity and allow myself to move into new technologies and areas just for the fun of it. ...

There are plenty of resources on the internet about testing in software engineering. However, as a data scientist, the code you need to test is often very different: The functions often input and output complicated data structures like dataframes, arrays, tensors, etc. The code is often very slow to run (e.g. a model that takes hours to train) Results of a function can be non-deterministic (e.g. a random forest model or a api call to a ML-service) The code is often very coupled to the data (e.g. a function that does preprocessing of a dataframe) The code is often very coupled to the model (e.g. a function that trains a model) We often need to test the whole pipeline (e.g. a function that trains a model and then evaluates it) Note this article is about functional testing and not evaluation of the model. The goal of testing is to make sure the code works and keeps working. The goal of evaluation is to make sure the model is good enough for the business case. ...

Introduction As more and more business problems can be solved using large language models (LLMs) in chat or QnA systems, the question of how to evaluate them has become increasingly important. Without proper evaluation, it is difficult to know if the system are providing real value to the business and users, or just misleading them and potentially inflicting harm. ...

I have always wanted to specialize into a field and become an expert. But I have always been drawn into new areas where I could see a need for focus to create value in the projects I have been involved with. Now I have decided to own it and instead go for the opposite. To the degree that it informed the name for my personal blog. This blog is about my personal journey towards growing into an archetype role that I refer to as a “Full Spectrum Data Scientist”. It is the professional path that I have decided to travel. ...