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The #1 Way to Make Your Data Science Team Succeed

Dataiku

Your data team is typically comprised of individuals from different backgrounds, with a variety of experience, skill, and knowledge levels, as related to data science and artificial intelligence. This makes communication and collaboration absolutely imperative to a successful data science project.

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DIY cloud cost management: The strategic case for building your own tools

CIO Business Intelligence

For CIOs who may need to customize their cloud cost information streams or manage a complex cloud estate, do-it-yourself cloud cost management may be the way to go. Here’s a look at why you might want to roll your own cloud cost solution, what makes a successful DIY approach, and how some leading organizations have already done so.

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4 core AI principles that fuel transformation success

CIO Business Intelligence

Here, in an extract from his book, AI for Business: A practical guide for business leaders to extract value from Artificial Intelligence , Peter Verster, founder of Northell Partners, a UK data and AI solutions consultancy, explains four of them. One reason implementing agile makes such a difference is the ability to fail fast.

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A Guide To Starting A Career In Business Intelligence & The BI Skills You Need

datapine

1) Why Shift To A BI Career? Does data excite, inspire, or even amaze you? Does the idea of discovering patterns in large volumes of information make you want to roll up your sleeves and get to work? Do you find computer science and its applications within the business world more than interesting?

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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Identifying the problem. Agreeing on metrics.

Marketing 362
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Start DataOps Today with ‘Lean DataOps’

DataKitchen

Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. DataOps can and should be implemented in small steps that complement and build upon existing workflows and data pipelines. Figure 1 shows the four phases of Lean DataOps.

Testing 246
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Hidden Mistakes that Companies Make in their AI Journey

CIO Business Intelligence

With a need for speed, organizations must also recognize the fact that almost half of AI projects never make it beyond the proof of concept stage. With a need for speed, organizations must also recognize the fact that almost half of AI projects never make it beyond the proof of concept stage.

Software 105