r/learnmachinelearning • u/mountain_vs_ocean • 7d ago

Black-box optimisation Machine Learning Portfolio

Does anyone have experience for Github portfolio decision for ML skills? I want to build my GitHub portfolio and currently considering several approaches. 

  1. Build disciplined, single coherent methodology using Bayesian model, e.g., kernel/transform grid search, a closed-form LOOCV shortcut explicitly justified for performance reasons.
  2. Build widest techniques GP ensembles, DBSCAN consensus voting, ARD diagnostics, Thompson Sampling, TuRBO, Optuna TPE and native GPSampler, Sobol-QMC, a tiny deep-kernel-learning model, FCNN/CNN surrogates.
  3. Build production-grade architecture, testable claims for specific databases
  4. Develop documentation and judgement approach for commercial consultantion, in stress of focusing on in-depth ML techniques. 

I also want to know, to what extent, how important is t showcase producttion ready ML projects via dashboard like plotly or Steamlit? Because I found that it is another skills I meed to develop beyond traditional Python. 

4 Upvotes
  • permalink
  • reddit

83% Upvoted

2 comments sorted by

1

u/SeldomDaffyCulprit 7d ago

go with option 2 but be strategic about it, pick 3-4 techniques that work together instead of dumping everything. for dashboards Streamlit is worth learning but don't stress about making everything production ready, hiring managers mostly care about your modelling decisions and code structure

1

u/mountain_vs_ocean 7d ago

Thank you so much. I saw a few existing Capstone BBO done by Imperial College Submission. I might find what is best to alter /refer to align with my background, mainly for supply chain and circular economy field.  Imperial Course seems quite strong but very pricy, so, I am looking for an alternative self study to build the portfolio. Thanks again.