Competitions: Beyond the Kaggle Leaderboard

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In this talk, Tatiana, Staff Software Engineer at LinkedIn, shares her journey from academic physics to becoming a Kaggle Master and winning the Sound Demixing Challenge. We explore how to use machine learning competitions as a strategic tool to build a high-impact career and bridge the gap between theory and production.You’ll learn about: * Turning competition code into professional GitHub repos. * Converting results into papers for NIPS and CVPR. * How LLMs are changing the benchmark for AI competitions. * Why hands-on implementation beats passive learning. * Using Topcoder and AI Crowd for research-driven goals. * Practical steps for your very first model submission.Links: * Rise: 3 Practical Steps for Advancing Your Career, Standing Out as a Leader, and Liking Your Life. By Patty Azzarello https://www.porchlightbooks.com/pages/author/Patty_Azzarello-16156396 - awesome book about why doing good is not enough, and what else you need to do to promote your career (same applies to competitions) * AICrowd - https://www.aicrowd.com/challenges * Grand challenges - https://grand-challenge.org/challenges/ * Kaggle competitions - https://www.kaggle.com/competitions * TopCoder challenge SpaceNet 9 - https://www.topcoder.com/challenges/9620f66a-767e-40ac-81d5-5cc61274b186(no current active competitions, but they appear) * Medium blog post with instruction - https://medium.com/data-science/writing-papers-tech-reports-after-kaggle-competitions-ee504fc0c4c1 * Kaggle Solution Write-Up Documentation - https://www.kaggle.com/solution-write-up-documentation * Evaluating Machine Learning Agents on Machine Learning Engineering - https://arxiv.org/abs/2410.07095 * Machine Learning Engineering Agent via Search and Targeted Refinement - https://arxiv.org/html/2506.15692v2 * AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench - chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2507.02554TIMECODES:00:00 Tatiana’s journey from academia to staff software engineer06:01 Machine learning applications in physics and signal processing09:13 Skill development and domain diversification on Kaggle13:35 Agentic AI benchmarks and automated competition entries17:43 Deep technical mastery versus leaderboard gamification23:04 Hands-on implementation and the illusion of learning26:01 Specialized platforms and fair competition environments31:35 Academic publications and research from silver medals35:24 GitHub repositories and engineering portfolio building39:02 Technical marketing via blog posts and LinkedIn43:25 Innovative approaches for academic conference submissions47:21 Research challenges at NIPS and CVPR workshops52:51 Medical imaging platforms and specialized recommendations57:46 First submission strategies for beginners01:00:56 Asynchronous collaboration and competition team dynamicsPerfect for data scientists and engineers looking to transition from academia or build a formal portfolio using Kaggle as a career-advancement tool.Connect with Tatiana: * Linkedin - https://www.linkedin.com/in/tatigabru/

Data Makers Fest 2026 Conference Interviews

At Data Makers Fest, a recurring theme was the tension between GenAI hype and production reality. Speakers stressed that classical ML, MLOps, evaluation, data quality, and governance remain essential—especially in regulated sectors like fintech and healthcare. Another strong theme was inclusivity: building AI that serves smaller languages, diverse communities, and practitioners beyond the English-centric ecosystem. Ryan Chaves. Head of ML at a Dutch fintech, Ryan focused on the gap between AI demos and production systems. He argued that classical ML remains critical for fraud detection and risk scoring, while GenAI works best as an accelerator on top of existing systems. He also emphasized storytelling, stakeholder communication, and mentorship as core engineering skills. Alp Öktem. Computational linguist and researcher Alp explored the imbalance between AI progress in English and low-resource languages. Through Mozilla Data Collective, he highlighted how open datasets, speech corpora, and synthetic data can expand AI access to underrepresented communities. His broader warning: fluent AI can still fail culturally, linguistically, and ethically. Agnieszka Kamińska. Working in pharmaceutical ML engineering, Agnieszka discussed extracting scientific knowledge from research documents into knowledge graphs. Her focus was reliability: LLMs help with entity extraction and relationship discovery, but trustworthy systems still require ontologies, validation layers, and production-minded engineering. She advocated a pragmatic middle ground between AI hype and skepticism. Nemanja Radojković. An MLOps engineer in finance, Nemanja reflected on how GenAI is changing software engineering itself. He argued that coding assistants improve productivity but risk weakening engineers’ understanding if overused. His central point: governance, reproducibility, and platform engineering will become even more important as organizations deploy AI agents at scale. Filipa Castro. Leading AI initiatives at Euronext, Filipa described how GenAI is integrated into regulated financial workflows. Her team uses LLMs to automate document-heavy operational processes while preserving human validation. Her broader message: successful enterprise AI depends less on flashy models and more on infrastructure foundations like CI/CD, monitoring, governance, and operational rigor. Beatriz Silva. As a student volunteer pursuing a master’s in data science, Beatriz represented the conference’s educational and community dimension. For her, the event was about access—networking with companies, exploring thesis opportunities, and connecting academic learning with industry practice. Her perspective highlighted how conferences like Data Makers Fest help shape the next generation of AI practitioners. Connect with speakers: Ryan Chaves. Head of Machine Learning at a Dutch fintech focused on fraud detection, risk systems, and production ML. LinkedIn [https://www.linkedin.com/in/ryan-c-g-chaves/?utm_source=chatgpt.com] Alp Öktem. Computational linguist and researcher focused on low-resource languages, inclusive AI, and open language datasets. LinkedIn [https://www.linkedin.com/in/alp-oktem/?utm_source=chatgpt.com] Agnieszka Kamińska. Machine Learning Engineer working on scientific knowledge extraction, knowledge graphs, and AI systems in pharma. LinkedIn [https://www.linkedin.com/in/agnieszka-kaminska-ds/?utm_source=chatgpt.com] Nemanja Radojković. Senior MLOps Engineer specializing in regulated financial systems, AI governance, and platform engineering. LinkedIn [https://www.linkedin.com/in/radojkovic/?utm_source=chatgpt.com] Filipa Castro. AI Lead at Euronext focused on enterprise GenAI systems, operational AI strategy, and financial services automation. LinkedIn [https://www.linkedin.com/in/filipafcastro/?utm_source=chatgpt.com] Beatriz Silva. Data science master’s student and conference volunteer exploring opportunities in ML and computer vision. LinkedIn [https://www.linkedin.com/in/beatriz-iara-silva/?utm_source=chatgpt.com]

22 de may de 20261 h 6 min

Competitions: Beyond the Kaggle Leaderboard - Tatiana Habruseva

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