The Analytics Factory

Data science, machine learning and AI are hot topics in today’s analytics landscape. Many breakthroughs have been made due to advances in algorithms and computing power. Organizations adopt these new technologies and use them to their own advance to improve sales, customer interactions or internal processes. But, they are also facing new challenges when developing and deploying analytics solutions. Not all solutions are easily scalable or can be deployed and run in an automated way, and more often than not point solutions are created using different tools on platforms which are hard to maintain. Moreover, the performance of analytical models degrades over time, requiring a different type of monitoring and maintenance. This session shows how to overcome these challenges and will address the following topics:
- how to industrialize analytical development processes for sustainable results
- pros and cons of lambda, kappa and other architectures
- how to design, build & scale your analytics factory

Target Audience: Data scientist, Analytics lead, IT/BI/Analytics architect, Platform Engineer, BI Manager
Prerequisites: Basic analytics and data analysis knowledge
Level: Advanced