Leveraging Spark and deep learning frameworks to understand data at scale

Vartika Singh is a field data science architect at Cloudera. Previously, Vartika was a data scientist applying machine learning algorithms to real-world use cases ranging from clickstream to image processing. She has 12 years of experience designing and developing solutions and frameworks utilizing machine learning techniques.

Alan Silva is a solutions architect and data scientist for Latin America (LATAM) at Cloudera, where he is focused on developing new solutions using machine learning algorithms and solutions, using Marvin as a workflow to support data science and machine learning projects. Alan has experience with a wide range of security systems and network technologies; his technical background includes cryptography, mathematics, network protocols, distributed systems, operational systems, application security, and secure software development. He holds an MSc in computer science from University Federal of São Carlos (UFSCAR), a postgraduate degree in cryptography and network security from University Federal Fluminense (UFF), and a BS in mathematics.

Alex Bleakley is the manager of the Machine Learning Solutions Architecture team at Cloudera. Alex combines core machine learning skills with 6 years experience implementing practical data solutions across multiple industries to lead a team focused on taking machine learning solutions to production at big data scale.

Steven Totman is the financial services industry lead for Cloudera’s Field Technology Office, where he helps companies monetize their big data assets using Cloudera’s Enterprise Data Hub. Prior to Cloudera, Steve ran strategy for a mainframe-to-Hadoop company and drove product strategy at IBM for DataStage and Information Server after joining with the Ascential acquisition. He architected IBM’s Infosphere product suite and led the design and creation of governance and metadata products like Business Glossary and Metadata Workbench. Steve holds several patents for data-integration and governance/metadata-related designs.

Mirko Kämpf is a solutions architect on the CEMEA team at Cloudera, where he applies tools from the Hadoop ecosystem, such as Spark, HBase, and Solr, to solve customer’s problems and is working on graph-based knowledge representation using Apache Jena to enable semantic search at scale. Mirko’s research focuses on time-dependent networks and time series analysis at scale. He loves to deliver data-centric workshops and has spoken at several big data-related conferences and meetups. He holds a PhD in statistical physics.

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Syed Nasar is a solutions architect at Cloudera. As a big data and machine learning professional, his expertise extends to artificial intelligence, machine learning, and computer vision, and he has worked with a number of enterprises in bridging big data technologies with advanced statistical analysis, machine learning, and deep learning to create high-quality data products and intelligent systems that drive strategy and investment decisions. Syed is a founder of the Nashville Artificial Intelligence Society. His research interests include NLP, deep learning (mainly RNN and GAN), distributed systems, machine learning at scale, and emerging technologies. He is the founder of Nashville Artificial Intelligence Society. He holds a master’s degree in interactive intelligence from the Georgia Institute of Technology.

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