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Early last month, we announced our agenda for Spark + AI Summit 2018, with over 180 selected talks with 11 tracks and training courses. For this summit, we have added four new tracks to expand its scope to include Deep Learning Frameworks, AI, Productionizing Machine Learning, Hardware in the Cloud, and Python and Advanced Analytics.

These new tracks have fostered some unique AI-related submissions that require massive amounts of constantly updated training data to build state-of-the-art models as well as ways of using Apache Spark at scale.

For want of worthy experience—whether picking a restaurant to dine, electing a book to read, or choosing a talk to attend at a conference—often a nudge guides, a nod affirms, and a review confirms. Rebecca Knight suggests in Harvard Business Review “How to Get Most Out of a Conference” by listening to what experts have to say, being strategic with your time, and choosing the right sessions.

As the program chair of the summit, and to help you choose some sessions, a few talks from each of these new tracks caught my attention: All seem prominent in their promise, possibility, and practicality; all seem to suggest how data and AI engender the best of AI applications because of unified analytics. I wish to share them with you:

AI Use Cases and New Opportunities:

Deep Learning Techniques:

Productionizing Machine Learning:

Python and Advanced Analytics:

Hardware in the Cloud:

Stay tuned for keynote announcements and favorite picks from other tracks by notable technical speakers from the community and big data practioners.

If you have not registered, use code “JulesChoice" for a 15% discount during registration. I hope to see you all there.

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