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Mahdi Mohseni

Mahdi Mohseni

Data / ML Scientist

Lübeck - Germany

© 2025 Mahdi Mohseni. All rights reserved.

About Me

I’m a Machine Learning (ML) Specialist and Data Scientist with expertise in two key areas:

i) Uncovering insights from complex, data-driven challenges through analytical modeling.

ii) Designing data processing pipelines to operationalize these models.

With hands-on experience in research and development, I specialize in machine learning, data analysis, and cutting-edge deep learning techniques such as Generative AI (GenAI) and Large Language Models (LLMs). My strong background includes designing and implementing end-to-end machine learning projects, with a focus on natural language processing (NLP), time series analysis, and image processing.

I occasionally publish posts on Medium and share insights on LinkedIn. My projects and code are hosted on GitHub, where I showcase my work. This website serves as a central hub, bringing together all my activities and providing a reference point to the other platforms.

Last Post: Time Series Forecasting - Quantile Forecasting - Quantile Loss

February 11, 2025

Traditional forecasting treats over- and under-predictions equally, but in real-world scenarios like financial markets, the costs can differ significantly. Quantile forecasting addresses this by estimating confidence intervals, offering a nuanced view of uncertainty. In my latest post, I explore how this method works and cover its advantages.

Tags: #time_Series_Forecasting #quantile_regression #loss_function #quantile_loss #pinball_loss

Logging Datasets in Machine Learning Experiments with MLflow

January 26, 2025

MLflow is widely recognized as a powerful tool for tracking machine learning (ML) experiments, enabling data scientists and ML experts to systematically log metrics, parameters, and models. However, its capabilities extend beyond these traditional use cases — MLflow can also be used to track datasets.

Tags: #mlflow #machine_learning #reproducability

Microsoft Azure Certificates — Part 1: Azure Fundamental AZ-900

July 11, 2024

I was offered a package of training programs covering several Microsoft Azure certificates. In a few posts, I’m going to explain each of these Azure certificates from the perspective of a data science and ML expert. The first post covers the Microsoft Azure Fundamentals certificate (AZ-900). I had access to official training by Microsoft and the learning material on learn.microsoft.com. Since the official learning material published on on learn.microsoft.com is the source for the exam, I will refer to the topics covered there.

Tags: #Azure #Az900 #Cloud #Machine_Learning

From Exploration to Modeling: When ML Models Fail in Action — A Case Study

July 7, 2024

I was asked to explore a car insurance premium dataset and predict a “fair” premium for each policyholder. The task included exploratory data analysis, feature engineering, model selection and comparison, and suggestions for model improvement. It was an interesting and insightful task, as our expectations sometimes fail and we can’t tackle the problem in a routine and straightforward way.

Tags: #explaratory_analysis #machine_learning #insurance

BentoML: Serving and Deploying Machine Learning Models

May 7, 2024

BentoML is an open-source platform for packaging ML models. It effectively simplifies the productionization of ML models by offering easy-to-use built-in features.

Tags: #betntoml #machine_learning #deployment