Media companies, ad agencies and brands run thousands of promotional campaigns every year, producing an immense amount of image and engagement data whose relationships to one another are complex and fragmented. With Digital Asset Management (DAM) systems making images more accessible, marketers are now faced with the more difficult problem of classifying images within their DAM and then making sense of what images and the content will stand out and be engaged with amidst the ad clutter. Utilizing AI and machine learning solutions to quickly collect and analyze data to create actionable recommendations on what content to select, produce or distribute is the new name of the game. Yet, organizations looking to use machine learning and AI face many challenges such as scaling infrastructure, adopting the latest ML/DL frameworks and deploying models into production.
In this webinar, you will learn how to overcome the challenges of accelerating advertising asset selection and campaign analysis with machine learning at scale. Neudesic will share how they use Databricks, Azure Cognitive Services and open-source technologies like Apache Spark to automate the labeling and analysis of thousands of images and to deliver a powerful search engine experience to end users through PowerB
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