Romeo Kienzler - Apache Spark 2: Data Processing and Real-Time Analytics: Master complex big data processing, stream analytics, and machine learning with Apache Spark

Romeo Keinzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics. His current research focus is on scalable machine learning on Apache Spark. He is a contributor to various open source projects and works as an associate professor for artificial intelligence at Swiss University of Applied Sciences, Berne. He is a member of the IBM Technical Expert Council and the IBM Academy of Technology, IBM's leading brains trust.

Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc degree in Computer Science. Before joining Fraunhofer FIT, he worked as a Researcher at Insight Centre for Data Analytics, Ireland. Before this, he worked as a Lead Engineer at Samsung Electronics' distributed R&D Institutes in Korea, India, Turkey, and Bangladesh. Previously, he worked as a Research Assistant at the database lab, Kyung Hee University, Korea. He also worked as an R&D engineer with BMTech21 Worldwide, Korea. Before this, he worked as a Software Engineer with i2SoftTechnology, Dhaka, Bangladesh.
He has more than 8 years' experience in the area of research and development with a solid understanding of algorithms and data structures in C, C++, Java, Scala, R, and Python. He has published several books, articles, and research papers concerning big data and virtualization technologies, such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce. He is also equally competent with deep learning technologies such as TensorFlow, DeepLearning4j, and H2O. His research interests include machine learning, deep learning, the semantic web, linked data, big data, and bioinformatics. Also he is the author of the following book titles:
Large-Scale Machine Learning with Spark (Packt Publishing Ltd.)
Deep Learning with TensorFlow (Packt Publishing Ltd.)
Scala and Spark for Big Data Analytics (Packt Publishing Ltd.)

Sridhar Alla is a big data expert helping companies solve complex problems in distributed computing, large-scale data science and analytics practice. He presents regularly at several prestigious conferences and provides training and consulting to companies. He holds a bachelor's in computer science from JNTU, India.
He loves writing code in Python, Scala, and Java. He also has extensive hands-on knowledge of several Hadoop-based technologies, TensorFlow, NoSQL, IoT, and deep learning.

Siamak Amirghodsi (Sammy) is a world-class senior technology executive leader with an entrepreneurial track record of overseeing big data strategies, cloud transformation, quantitative risk management, advanced analytics, large-scale regulatory data platforming, enterprise architecture, technology road mapping, multi-project execution, and organizational streamlining in Fortune 20 environments in a global setting. Siamak is a hands-on big data, cloud, machine learning, and AI expert, and is currently overseeing the large-scale cloud data platforming and advanced risk analytics build out for a tier-1 financial institution in the United States. Siamak's interests include building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.

Meenakshi Rajendran is a hands-on big data analytics and data governance manager with expertise in large-scale data platforming and machine learning program execution on a global scale. She is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. She not only understands the underpinnings of big data and data science technology but also has a solid understanding of the human side of the equation as well.
Meenakshis favorite languages are Python, R, Julia, and Scala. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale. In her free time, she dabbles in software engineering management literature, cognitive psychology, and chess for relaxation.