The Future Of KaggleWhere we came from and where we’re going

kaggle.com/benhamner@benhamner

Our mission is to help the world learn from data

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We got started running supervised learning competitions

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Since 2010, we’ve run

● 240 general competitions● 1,610 university classroom competitions

We’re now doing this at scale

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This has attracted a talented and diverse community

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We’ve taught hundreds of thousands machine learning

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We’ve pushed the state of the art forward

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● What techniques work well● How people win competitions● Why our community participates● What major pain points data scientists hit● How we can help data scientists ameliorate these pain points

We’ve learned a tremendous amount along the way

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Great data scientists optimize the entire ML workflow

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GBM’s and deep neural networks are incredibly effective

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Model ensembling almost always ekes out gains

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Successful participants avoid overfitting

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We’ve seen major pain points

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Today’s practices are like programming in assembly

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Beside software engineering tools, ML tools feel like they came from the stone age

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Accessing data is tough

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Getting high quality data is even tougher

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Cleaning data is painful

Essay: “This essay got good marks, but as far as I can tell, it's gibberish.”

Human Scores: 5/5, 4/5@benhamner

Data leakage is common and subtle

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Going from research to production can be brutal

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Reproducing work takes days to months

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We can do better than this

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Accessing data should be seamless

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You should never need to repeat work others have done

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A single command should reproduce everything start-to-end

> make all

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Making a successful one-line update should take seconds

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Helpful metadata shouldn’t stay buried in minds or emails

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Best practices should be easy defaults, not complicated custom contraptions

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We’re changing this

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We’ve launched two new products: Kernels and Datasets

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We recently joined Google Cloud to accelerate our growth

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Datasets, Kernels, and Competitions have an exciting future

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The world’s data will be accessible with a common interface

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That captures the important code and metadata on top of it

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A central searchable hub for your organization’s data

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A kernel is an atom of reproducible data science

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Kernels will be your continuous integration server for data

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We’ve started running code competitions

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● Backtested time series● Live data feeds● Reinforcement learning● Generative modeling● Adversarial learning● Machine learning under computational constraints● Sensitive datasets

This will enable exciting new competition formats

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