Tools for Competitors in Machine Learning Competitions

Machine learning competitions are a relatively new phenomenon.

It appeared as a result of the development of artificial intelligence technologies.

At the moment it is actively developing and attracting many interested people.







Benefits for the organizers of the competition:







  • A large number of qualified people who work on their task and try to solve it better than others
  • Relatively small (in comparison with hiring specialists) financial costs
  • The solution to the problem, the highest quality and most suitable for it


And the competitors also benefit:







  • Public recognition of high qualifications
  • Cash prizes
  • And just the pleasure of participating and winning


In this article, I want to consider several tools that can help participants organize the process better and more efficiently, increase the likelihood of winning, and, in general, become a more qualified specialist.







Let's get started!







Determined







A platform for training deep learning models.







  • Accelerated training of models using state-of-the-art distributed training, without changing the model code
  • Automatic search for high quality models, with advanced hyper-parameter settings - from the creators of Hyperband
  • GPU GPU,
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  • DL-
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Compose







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  • Featurepools
  • EvalML


Featuretools







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EvalML







AutoML , .







  • Featuretools Compose end-to-end ML-


Pandas Profiling







DataFrame Pandas.







  • df.describe() — df.profile_report()
  • HTML-
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Tpot







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  • Python
  • Scikit-learn


Shap







- ML-.









Feature-engine







ML-.







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Lale







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Biome







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DataSketch







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PyTextRank







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Joblib







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  • Pickle ,


Shampoo







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Michelangelo







Uber.







  • API


Hasty.ai







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Cortex







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  • API


Weights & Biases







.







  • -
  • -


SpeedRun







ML-.







  • Weights & Biases
  • - Weights & Biases
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  • matplotlib


Great Expectations







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Keras Tuner







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NanoEdge AI Studio







AI-, MCU C .







  • MCU C
  • Arm Cortex-M
  • (1-20kB RAM/Flash)
  • (1-20ms M4 80MHz)
  • AI
  • C
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  • ML


LabelBox







End-to-end .







  • API (Python, GraphQL) SDK
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  • API


LabelML







ML- .







  • (2 )
  • , -s, -
  • Tensorboard
  • API


PyCaret







Low-code ML-.







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  • ( 60 )
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CometML













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ClearML







ML- (MLOps).







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  • Classical music
  • Good mood


Conclusion.







Of course, just describing the tools is not enough to always win.

Success depends on many other factors - knowing where and when to use a particular tool or not, what are the limitations, how the tools can be combined, etc. etc.

I hope that nevertheless this article will be useful for you and your participation in the competition will become more fruitful and effective.







Forward to victories!








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