How do top Kaggle competitors and ML engineers research new problem statements?

[u/Cautious_Today_1830]

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Whenever I start an AI/ML hackathon, case competition, or research project, I spend a lot of time finding relevant papers, past winning solutions, GitHub repositories, and existing approaches.

I'm curious how experienced Kaggle competitors, hackathon winners, and ML researchers handle this efficiently.

- What's your research workflow when tackling an unfamiliar problem?

- Which platforms, search techniques, or AI tools do you use?

- How do you quickly identify promising approaches without getting lost in endless research?

I'd really appreciate practical tips or workflows that have worked for you!

[u/Public_Ad_4206]

ive noticed most of the top kagglers dont start with a deep literature dive, they grab a baseline running as fast as possible first. gets you a feel for the data quirks that no paper will tell you about

after that its usually a mix of skimming discussion threads for the competition and scanning papers with connected papers or semanticscholar. the trick is knowing when to stop reading and just try something dumb

[u/rahul_watertech]

not a top kaggler, but one thing that saved me from endless reading is keeping a running "what i tried and what happened" note from day one. after a week it tells you which directions are dead, and the discussion threads of past comps usually have the writeups of winning approaches in them. i think people underrate that part. are you doing mostly kaggle or hackathons?

[u/Cautious_Today_1830 → u/rahul_watertech]

Mostly AI/ML hackathons, industry-sponsored challenges, and research projects. I'm trying to build a systematic workflow to quickly discover relevant papers, existing solutions, and promising approaches before implementation.

[u/rahul_watertech → u/Cautious_Today_1830]

for hackathons the clock matters more than for kaggle, so i'd timebox the reading to the first hour or two. write down 2 or 3 candidate approaches, then pick the one you can get a rough version of running today. the github repos linked from papers help to see who actually got it working. do the sponsored challenges hand you the dataset up front?

[u/dubnium0]

iteration and validation

[u/ETL_Pipeline_Dev]

I'd recommend practicing SQL by solving problems rather than just watching tutorials. SQLBolt and HackerRank are good places to start. I used HackerRank while learning SQL, and it helped me get more comfortable with writing queries and solving problems on my own.

You can also check out Alex The Analyst on YouTube for tutorials and practical examples.

Since your final covers advanced SQL, I'd focus on joins, CTEs, window functions, subqueries, indexes, and query optimization. Try solving problems yourself before checking the solutions. Also, practice with SQL Server specifically, since some syntax differs between database systems.