Hello! I’m Berry, an AI Engineer with over a decade of experience in Artificial Intelligence and Data Science. My passion lies in leveraging AI to help businesses become more efficient and competitive.
What I Do#
In my professional journey, I’ve had the opportunity to work on a variety of projects across diverse industries. Here are some of the key areas I specialize in:
- š¤ AI and Automation Consulting: Helping businesses understand and integrate AI and automation solutions.
- š AI Training: Providing training sessions to help teams develop AI skills.
- š AI Product Development: Building custom AI products from concept to deployment.
- š£ Coaching: Offering one-on-one coaching to help professionals grow their AI expertise.
Current Focus#
My current focus is on scaling AI training programs to help more businesses become efficient using AI. By providing comprehensive training sessions and hands-on workshops, I aim to empower organizations to leverage AI technologies effectively.
I am also working on a top-secret project for the Dutch government, pushing the boundaries of AI innovation. š¬šµļøāāļø
Academic Journey#
I hold a master’s degree in Data Science from the University of Amsterdam, where I conducted significant research in the field of AI. My research has been published on various platforms. One notable project was on domain adaptation in transformer models for question answering on Dutch government policies.
Previous Experience#
I’ve collaborated with a range of clients, delivering impactful AI and data solutions that drive business success:
Dutch Government
Worked on setting up local LLMs and a scheduling mechanism for other teams to efficiently utilize our servers.
This improved resource allocation and increased productivity across multiple departments.
Technologies used: Python, Docker, Kubernetes, and custom scheduling algorithms.
KPN
Created parsing logic in Python and Scala, and enhanced the monitoring system for Dutch railways using Prometheus and Grafana.
This resulted in improved operational efficiency and real-time insights into railway operations.
Technologies used: Python, Scala, Prometheus, Grafana, and big data processing tools.
VodafoneZiggo
Assisted in migrating data pipelines, ensuring a smooth transition and reliable data flow.
This modernization effort improved data processing speeds and reliability of analytics.
Technologies used: Apache Spark, Hadoop, and cloud migration tools.
Mileway
Developed a document management system to automatically detect real estate documents, streamlining document handling and increasing accuracy.
This system significantly reduced manual processing time and improved data extraction accuracy.
Technologies used: Machine Learning, OCR, Python, and document processing libraries.
Rabobank
Helped develop the data marketplace and set up data infrastructure, enabling better data management and accessibility.
This resulted in improved operational efficiency and real-time insights into railway operations.
Technologies used: Python, Pulummi, Spark, Azure pipelines.
Ahold
Created dashboards that provide insightful data visualizations, aiding in informed decision-making.
This modernization effort improved data oversight of analytics.
Technologies used: Power Bi.
Unicef
Built an Azure data platform from scratch to enhance marketing efforts, significantly improving campaign effectiveness.
This platform increased markering effort and allows for microcampaigns resulting in a doubling of donations.
Technologies used: Azure, Dynamics, Python, and document processing libraries.