Watch (or rewatch) past webinars brought to you by Dataiku, the platform democratizing access to data and enabling enterprises to build their own path to AI.

Crash Course in Data Architecture

Data architecture is the foundation of every organization’s data strategy, and everyone can benefit from understanding the ways data moves between teams and flows into data projects to yield insights. Learn about the key architecture terms and different priorities regarding security and scalability in this crash course.

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Machine Learning Based Fraud Detection in Banking

Fraudulent actors are always looking for new ways to subvert legitimate transaction systems; traditional rules-based approaches are no longer sufficient (or efficient enough) to combat fraud. In this webinar, we’ll discuss best practices and examples on how machine learning can improve fraud detection capabilities.

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Data Transformation at GE Aviation

This webinar is a Q&A format with Jon Tudor, Sr. Manager Self-Service Data and Analytics and Somesh Saxena, Product Owner of Dataiku and Alation, at GE Aviation. It covers the how and why behind the company’s data transformation.

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Defining ROI for Data Initiatives

Calculating the Return on Investment (ROI) of your data initiatives is critical to activating your data; if you can’t show that data initiatives are valuable, there will be resistance across the organization to implementation, causing you to miss out on opportunities and lag behind the competition.

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Human-Centric Artificial Intelligence: What and Why? | Dataiku x Gigaom

How is AI impacting humans, in their jobs, and how should it? Can AI be responsible, transparent and trustworthy? What’s the best way to monitor and mitigate bias in AI models? And what impact will automated machine learning (AutoML) have on these questions, and on the role of Data Scientists? This webinar by GigaOM and Dataiku has the answers.

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Automating AI in the Data-Driven Enterprise | Dataiku x GigaOM

In this webinar, you'll learn about the science and power behind automated machine learning (AutoML), and how to make it actionable and accessible to all your data workers, while combating bias, drift, and other AI pitfalls.

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