Emeli Dral
Co-founder & CTO at HARBOUR.SPACE
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolEmeli Dral is a Co-founder and CTO with nine years of experience building ML-first products and teams, currently leading Evidently AI to deliver open-source observability tools for monitoring models and data pipelines. She has led 50+ applied ML projects across industries and co-founded Mechanica AI and Data Mining in Action, which became the largest offline data science program in the CIS. An experienced educator, Emeli co-authored Coursera specialisations with 140K+ combined enrollments and has taught industrial and executive ML courses at top institutions including MIPT and Yandex School. Her hands-on contributions to the evidently open-source project emphasize practical model diagnostics and visualizations, reflecting a focus on making ML trustable in production. Based in London, she blends product-focused engineering, research-driven pedagogy, and startup scaling experience to bridge ML research and operational reliability.
9 years of coding experience
6 years of employment as a software developer
Master's degree Applied Mathematics and Informatics, Master's degree Applied Mathematics and Informatics at Peoples’ Friendship University of RussiaMaster's degree Computer Science, Master's degree Computer Science at Yandex School of Data Analysis
Russian, English, French
Github Skills (8)
scikit-learn10
data-visualizations10
pandas10
machine-learning10
data-visualization10
data-visualisation10
python10
scikit10
Programming languages (3)
Github contributions (5)
evidentlyai/evidently
Nov 2020 - Jan 2023
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Role in this project:
Data Scientist
Data ScientistContributions:59 releases, 5 reviews, 383 commits in 2 years 2 months
Contributions summary:Emeli primarily contributed to the development and application of data analysis and machine learning models within the project. Their work involved modifying existing code to generate data drift reports with customized histograms for data visualization. They integrated libraries such as scikit-learn and Evidently for generating data drift reports for the Boston dataset. The user also worked on adding visualizations to existing datasets.
ml-modelspythonmonitordiscordjupyter-notebook
emeli-dral/evidently_experimental
Jul 2023 - Jan 2025
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Contributions:47 pushes in 1 year 6 months
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