Previous post: Passing the AWS Certified Cloud Practitioner exam: An ML Engineers perpsective
Validation: Credly
As a machine learning engineer, I recently passed the AWS Certified Solutions Architect Associate certification to expand my cloud architectural competencies. Having worked primarily in S3, EC2, and SageMaker, I knew this exam would force me to stretch into less familiar territory critical for effective ML solutions architecture. And it certainly delivered on that front through reinforced learnings, new discoveries, and expanded awareness.
Last year I took my first run at Advent of Code using C++, managing to earn 14 stars over 7 days before tapping out. While a valuable learning experience, it showed me I still have room to grow as a programmer. This year I plan to attempt the challenges in Rust to expand my skills in a different systems language.
My goal is to better my overall performance from last year.
Originally Posted on Manchester Digital
Gaining public trust is key for widespread adoption of AI systems. Users need confidence in an AI’s safety, fairness and integrity before relying on its outputs. Enhancing transparency and explainability of these systems helps build more trust.
A transparent AI clearly conveys details about its training data sources, development methodologies, and decision-making processes. An explainable AI elucidates the reasoning and logic behind how it generates specific outputs or recommendations.
For those not familiar with advent of code:
Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as interview prep, company training, university coursework, practice problems, a speed contest, or to challenge each other.
I, as do many others, find it a great experience to reinforce problem solving and algorithm design, I especially like using it to reinforce new langauges, and thinking in them.
Nothing to see yet. Although probably my proudest achievement to date. Also doubles as an introduction to lots of concepts applied in other work.
But check out the poster, the slides, or the thesis. for more details.
Overview The report describes efforts to map the prevalence and risk of onchocerciasis (river blindness) infection across Gabon in order to focus ivermectin treatment programs. Onchocerciasis causes severe itching, skin lesions, and vision impairment. Over 99% of cases are in sub-Saharan Africa. The analysis uses data collected on infection prevalence across 59 survey villages in Gabon, along with geographic data like elevation and water proximity. Geographic information system (GIS) concepts and geostatistical analysis are used to estimate infection risk across Gabon.
I recently worked on an interesting project using deep learning for semantic segmentation of multispectral satellite images. The goal was to accurately categorize each pixel into one of 10 different classes like buildings, roads, trees, water bodies, vehicles etc. This allows us to create detailed maps from the imagery. The dataset came from the DSTL Satellite Imagery Feature Detection challenge on Kaggle.
Since this is a kaggle competition, its worth adding references/inspiration before the main text.
The report introduces tevt, an R package for threshold estimation methods and diagnostic plots for extreme value theory (EVT). It aims to fix issues with existing EVT packages and provide the most comprehensive collection of threshold methods. Background
Provides theory on EVT, the generalized Pareto distribution (GPD), and importance of choosing a threshold above which the GPD approximates the tail. Discusses bias-variance tradeoff. Introduces kernel density estimation (KDE) which is useful for nonparametrically estimating densities without making assumptions.