Data Scientists and the AI Revolution

Data scientists know what tools and technologies work best for any given task, from prototyping machine learning-based pipelines to deploying scalable data-based services across the enterprise.

Data scientists want to work faster and smarter everywhere: data access, data pre-processing, feature engineering, model training, and testing.

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But data scientists also have a lot to gain from:

  • Enabling a culture of operationalization: Pushing to go from prototypes to production is critical to success, and it starts with data scientists. A culture of operationalization is how data scientists will build trust – and prove – that data science brings real business value.

My greatest fear about data science…is that the penny will drop and people will realize that a lot of what has been promised is not yet possible.

Shaun McGirr
Head of Data Science and Business Intelligence, Cox Automotive

  • Cross-team collaboration: When data scientists work directly with subject matter experts, projects are better aligned with business objectives and closer to generating value for an organization (read: support for data science initiatives – and data scientists themselves – will continue to grow). Plus, effectively incorporating business analysts in data projects can help shift time spent on data cleaning and preparation.
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  • Responsibility and white-box approaches: These subjects go far beyond ethics (though also critically important for data scientists) and touch on an approach to data science that is sustainable for years to come. For data scientists themselves, that means involving the right people, asking the right questions, and holding themselves responsible for practicing responsible AI.
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  • Automation: Automate data-preprocessing, feature engineering, and model training to quickly discover and build the machine learning and AI services needed to deliver value from data.
  • Operationalization: Create a one-stop-shop in a visual interface to load models and serve requests in just a few clicks. Say goodbye to human bottlenecks and hello to quickly deployed models and user independence.
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  • Modeling: Save time with parallel model building and AutoML functionalities – see the performance of hundreds of models competing in real-time, and quickly identify the obvious winners (and losers).
  • Reproducibility: In Dataiku, deployed code is reproducible – even unmaintained projects won’t fall through the cracks and fail.
  • Flexibility and White Box AI: Create custom reusable components (plugins) out of existing code and leverage the tools (R, Python, Scala, etc.) and libraries (MLlib, H2O, XGboost, Scikit Learn, etc.) from the rich and ever-growing open-source ecosystem to build and run data services from scratch.
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MLOps as a Critical, Emerging Role

As AI initiatives expand, MLOps is cornerstone in ensuring deployed models are well maintained, performing as expected, and not having any adverse effects on the business.

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Go Further

Posing as a Data Engineer

Posing as a data engineer is a data scientist's story by Kenny Ning of Better Mortgage, showing the evolution of the role.

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Automation for Admin Tasks

Many companies are still confronted with the oldest of administrative nightmares: piles and piles of mail. A perfect problem to solve with data science!

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Follow Dataiku's Data Scientists

See what the data scientists at Dataiku are up to on Data from the Trenches, which explores the nitty gritty details of data science.

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See a Demo of Dataiku

Easily skip through or access the sections you want to see from data preparation to machine learning and automation with this choose-your-own-adventure style demo.

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