Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do you deploy these ML model to a production environment? How do you embed what you’ve learned into customer facing data applications? In this talk I will discuss best practices on how data scientists productionize machine learning models, do a deep dive with actual case studies, and show live tutorials of a few example architectures and code in Python, Scala, Java and SQL. Session hashtag #SFexp5
I am a seasoned data and analytics professional with 18 years of experience both as an architect and team leader. Experienced in data architecture, data management, business transformation, data strategy, data science, traditional machine learning, machine learning platforms, open source technologies and cloud.