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AI Projects: Lifecycle and Best Practices

As companies around the world look to get a jump on AI efforts, there’s one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects?

Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this webinar, we’ll share a comprehensive framework for:

1) Choosing relevant projects: defining needs from business lines and cost/benefit analysis.
2) Advanced project scoping: aligning stakeholders, anticipating key steps, defining success metrics.
3) Operationalization of AI projects: challenges and best practices.

In this session, we will walk you through the methodology we use at Dataiku to guide and accompany our customers in defining and executing a roadmap of high-value AI projects.
Recorded Apr 21 2020 40 mins
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Presented by
Vincent De Stoecklin, Customer Success Director, APAC
Presentation preview: AI Projects: Lifecycle and Best Practices

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Your Path to Enterprise AI
Dataiku is the centralized data platform that moves businesses along their data journey from analytics at scale to enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies.

Customers like Unilever, GE, BNP Paribas, Santander use Dataiku to ensure they are moving quickly and growing exponentially along with the amount of data they’re collecting. By removing roadblocks, Dataiku ensures more opportunity for business-impacting models and creative solutions, allowing teams to work faster and smarter.

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  • Presented by: Vincent De Stoecklin, Customer Success Director, APAC
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