Click prediction algorithms are essential & used extensively for sponsored search and real-time ad bidding. The current advertising scenario often makes the predictions redundant, if they are not near real-time.

In this webinar, we explain the application of Spark’s machine learning pipeline for predicting Click Through Rates (CTRs). We also cover the many challenges you may face & how to overcome them.

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Key takeaways

Predictive algorithm

How to overcome challenges faced while running predictive algorithms on
sparse data

Machine learning

What are the advantages of using Spark machine learning pipeline API for
CTR prediction

Non-distributed frameworks

Find out the limitations of using
non-distributed Machine
Learning frameworks

Limited seats available



Suresh Babu

Chief Revenue Officer

Priding himself on execution excellence, Suresh drives Imaginea’s business growth through robust sales, marketing & business strategies.  In a career spanning twenty years, he has had senior leadership roles, driving business growth for Hitachi Consulting/Sierra Atlantic, SoftSol, CSS Corp, and HP/Compaq.  He is a proven leader with a successful track record, known for his ability to build high performance teams and drive profitability and growth.

Sachin Tyagi

Head – Data Analytics

An alumnus of IIT Guwahati with over ten years of industry experience, Sachin spearheads the data engineering and analytics practice at Imaginea. He combines data science and data engineering expertise to solve complex problems in big data and machine learning. Sachin has been pivotal in implementing Apache Spark solutions for several FAST 5000 companies in predictive recommendation, anomaly detection & contextual Search.