Top financial institutions are adopting AI in across every area of the business, but ad-hoc process and tooling causes significant pain.

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As the number of AI-powered projects in financial institutions reaches an all-time high and demand increases, an increasingly large proportion of projects are being blocked from progressing to production.

AI visionaries within these businesses have identified the fact that you can't simply apply software DevOps approaches to ML because ML is fundamentally more complex than software engineering.

DevOps for ML, or MLOps - the next paradigm shift in AI - is currently underway to make AI governance, management and collaboration as fast and safe as DevOps has made software engineering in the enterprise.

Key AI challenges

  • Productivity is low due to the high switching cost as ML engineers waste time reconfiguring their development environments from one project to the next.
  • Collaboration is extremely difficult using ad-hoc communication, especially when teams are spread globally across many timezones.
  • Failure to have a consistent place and format to store ML projects and their artifacts (e.g. data, models, hyperparameters) causes challenges when models need to be handed over between data scientists.
How can Dotscience help?

Dotscience provides a collaborative data and model development environment that accelerates AI project delivery and ensures that projects are not blocked from being deployed into production by insufficient process and tooling. By integrating data engineering, model development, deployment to production and statistical monitoring, Dotscience delivers seamless and fast progression of models into production and helps you keep them performing reliably.