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The State of AI Development.

Is your team working with data and models? You may have problems you don't even realize exist, because these problems are normal in our industry today.

  1. Wasting time

    Setting up your development environment, keeping data in sync across a bunch of machines, reproducing a colleague's result; lots of time is wasted in typical ML projects.

  2. Not collaborating

    Data scientists often avoid collaborating because it's difficult with current tools, but the benefits of avoiding siloes are well documented.

  3. Manual tracking

    If you are tracking results of experiments, or mappings from data to models in a spreadsheet, you're likely to make mistakes and not capture the full picture.

  4. No reproducibility or provenance

    By overwriting data or emailing notebooks you're creating future risk of not being able to fix a model that is buggy.

  5. No automated testing

    Without automated tests it's easy to accidentally deploy broken models into production, as described in What's your ML test score?

  6. Snowflake deployments

    If you're manually deploying models into production by copying files, you risk losing track of what's running where and how it was created.

  7. Unmonitored models

    If you're not testing model drift and statistical behavior in production, your models could go haywire and you might not notice until real damage has been done.

  8. Technical debt

    As Google describes in The High Interest Credit Card of Technical Debt, ML systems are particularly susceptible to new and interesting forms of tech debt.

These issues make ML projects fail and create financial and reputational risk. Solving them promises more productive, effective AI teams and better and safer models.

Headshot of Anders Åström, Datascience Manager at a global Technology Consulting firm

“The processes and tools for collaborating and maintaining ML projects at industrial scale is not yet as mature as for traditional software projects. The ML workflows pose several additional challenges that doesn’t perfectly fit into Software DevOps processes. I am excited to work with Dotscience to tackle these challenges in our upcoming project, as they are actively focused on making collaboration structured and centralised so that it scales to much larger team and project sizes.”

Anders Åström, Datascience Manager at a global Technology Consulting firm