tailwindlabs / tailwindcss
github.com·23h ago
Developer Tools
Kaggle Blog·Sep 9·5 min read
I trained a model. What is next?
TL;DR
Machine learning practitioners often finish training a model without knowing how to proceed effectively or learn from competition experience. Vladimir Iglovikov, a Kaggle Grandmaster (top-ranked competitor), shares post-training advice and lessons learned from participating in ML competitions.
✦ Why It Matters
Learn structured post-training workflows and skill-building strategies from a top-ranked ML competitor's experience.
Key Takeaways
- Establish a systematic workflow for steps after model training completes.
- Participate in ML competitions to build practical machine learning expertise.
- Document and reflect on lessons learned from each completed project.
- Seek mentorship or guidance from experienced practitioners early in your career.
Full Summary
Vladimir Iglovikov achieved Kaggle Grandmaster status—a recognition for top-performing competitors in machine learning competitions—by participating in numerous ML challenges on Kaggle and similar platforms. His experience revealed a gap: many practitioners complete model training but lack guidance on subsequent steps and how to extract lasting knowledge from competition work.
Iglovikov's post synthesizes lessons he wishes he had received earlier, covering practical advice for post-training workflows and skill development. Rather than presenting novel algorithms or techniques, the work distills experiential knowledge from high-level competition performance into actionable guidance.
The value lies not in measured performance improvements but in providing a structured perspective on how to transition from training completion to sustained learning and professional growth in machine learning.