Data Science · Machine Learning · GenAI
I'm a cloud and AI architect with 17 years in IT, lately across deep learning and generative AI. This is where I share the projects I build and what I learn along the way.
Writing
Exploratory Data Analysis(EDA) is the process of understanding and studying the data in detail to discover patterns, spot anomalies and outliers to maximize the insights we derive from the data set. We use data visualization techniques to recognize patterns and draw inferences that are not readily visible in the raw data.
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To make the case, let me introduce you to Anscombe’s quartet. It comprises four datasets which have similar statistical summary. They have the same measures of central tendency, correlation, and even the same linear regression model. They look alike in every way. However, you throw in data visualization and a completely different story begins to emerge. In the following blog, you would find detailed notes on the dataset and it’s visualization using python libraries.
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Q-Q plots are also known as Quantile-Quantile plots. As the name suggests, they plot the quantiles of a sample distribution against quantiles of a theoretical distribution. Doing this helps us determine if a dataset follows any particular type of probability distribution like normal, uniform, exponential
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My Master's thesis explored automated identification of medicinal plant species from leaf imagery. I built an image-classification pipeline using convolutional neural networks and transfer learning from pre-trained backbones, adapting them to a specialised botanical dataset to reach reliable accuracy on a fine-grained recognition task.
Design and delivery of generative-AI capabilities: from retrieval-augmented generation over internal knowledge to LLM-assisted workflows that reduce manual effort. Focused on grounding model output in trusted data, evaluating quality, and shipping something dependable rather than a demo.
A postgraduate project boosting sales efficiency for an EdTech firm: feature engineering over operational data, model building to identify the drivers of performance, and turning the results into recommendations the business could act on.
About
I care about the parts of data science that make models actually useful: clean data, honest evaluation, and dependable delivery. Here's where I focus.
Taking models from notebook to something dependable: data pipelines, evaluation, and the unglamorous work that makes ML actually useful.
Building with large language models: retrieval, grounding, and evaluation so that GenAI features are trustworthy, not just impressive.
Operationalising data science on Microsoft Azure and Azure Machine Learning, backed by an MCSE in Cloud Platform and Infrastructure.