Nikolay Manchev (Big Data Technical Specialist, IBM Analytics) will cover IBM's commitment to Spark, how IBM SystemML can help advance machine learning, how to roll out machine learning algorithms in production and how to achieve shorter development ties and increased algorithm performance
Nikolay will cover:
IBM’s commitment to Spark and how IBM is working with the community to enhance Spark
IBM SystemML – a breakthrough technology aimed to advance machine learning at the core of the Apache Spark project
Common problems encountered by data scientists when they roll out machine learning algorithms into production
How SystemML can transparently contribute to shorter development times and increased algorithm performance
Nikolay has over 10 years of database experience and has been involved in large scale migration, consolidation, and data warehouse deployment projects in the UK and abroad. He is a speaker, blogger, author of numerous articles and a book on advanced database topics. For the last three years Nikolay has been working exclusively in the big data (Hadoop) space with focus on Spark and machine learning. He has an M.Sc. in Software Technologies and is working towards an M.Sc. in Data Science.
With the mechanics done, Jan will explain how to use (deep) neural networks that can be very easily trained to recognize patterns in the ingested data.
You will learn the advantages and traps of designing distributed domains, data and computation: systems that may become the next generation of financial systems, bringing elasticity, resilience and responsiveness. You will look in particular at systems that consume data from IoT / wearables, and that perform immediate and batch analyses.
Jan Machacek is a passionate technologist with hands-on experience of the practical aspects of software delivery (architecture, quality, CI, CD), the project management approaches (applying the principles of agile project management), and mentoring and motivating engineering & business teams.