Leveraging Spark ML for Real-Time Credit Card Approvals - Databricks

Leveraging Spark ML for Real-Time Credit Card Approvals

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This tech talk deals with how we leveraged Spark Streaming and Spark Machine Learning models to build & operationalize real-time credit card approvals for a banking major. We plan to cover ML capabilities in Spark and how a typical ML pipeline looks like.

We are going to talk about the domain and the use case of how a major credit card provider is using spark to calculate card eligibility in real-time. We’re also going to share the challenges faced by the current system and how spark is a good fit to solve these kinds of problems.

We will then take a deep dive on the different tools that were used to design the solution and the architecture of the system. Here, we will also be sharing of how a spark based workflow was created to address various aspects like reading from Kafka, parsing, data enrichment, model selection, model scoring, rule execution to conclude the recommended output.

Finally, we’re also going to talk about the key challenges, learning and recommendations when building such a system and taking it to production.

Session hashtag: #Ent6SAIS



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About Anand Venugopal

Anand is a techno-business leader at Impetus Technologies providing product strategy, product marketing and sales leadership for the StreamAnalytix business at Impetus. He is focused on evangelizing and delivering business value from big data and fast data analytics to Fortune 1000 enterprises. Having spoken at numerous big data conferences on a range of topics including big data use cases, ROI, real-time streaming analytics, enterprise big data bus – Anand is a well-known thought leader in the big data ecosystem. He brings 22+ years of software technology, architecture and go-to-market experience in hi-tech, telecom, mobile, gaming and enterprise big data and analytics systems.

About Saurabh Dutta

Saurabh leads multiple engineering and R&D efforts for new and upcoming features in StreamAnalytix. He is one of the early team members who bootstrapped the product StreamAnalytix. His areas of expertise include big data, advanced analytics and cloud computing. He is responsible for analyzing customers' business challenges and create generic solutions to fit across industries and domains.