Presented by Paige Roberts, Open Source Relations Manager at Vertica

Advanced analytics and machine learning can be used to reduce churn, increase risk analysis accuracy, detect and even prevent fraud, but only once it makes it into production. Legacy machine learning workflows had data science teams working on their own, with samples of data, and a completely different technology stack from the one used to operationalize useful models.

You can find dozens of articles on-line that say ""90% of machine learning projects fail."" Beyond that, 30 to 60% of the few ML projects that made it to production took 3 months to a year to get put to work. Since data science is a cost center for organizations until those models are deployed, the need to shorten and streamline the process from ideation to production is essential.

To get the benefits of AI and ML, do you need to add yet another technology to already bloated stacks? Hire more people with yet more expensive skillsets? There may be a way for organizations to get machine learning into production faster with something nearly every company already has: a good analytics database.

Learn how to:
• Enable data science teams to use their preferred tools – Python, R, Jupyter – on multi-terabyte data sets
• Provide dozens of data types and formats at high scale to data science teams, without duplicating data pipeline efforts
• Make operationalizing new machine learning projects just as straightforward as new BI dashboards
• Get machine learning projects from finished model to production money-maker in minutes, not months

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