Predictive analytics and machine learning are on the agenda of every organization across the globe. PAML has made it to the boardroom and is deemed central to business strategy and transformation. The transition of analytics from end-user computing to an enterprise capability generates new pressure for governance and support—both from IT and from the business. How do you avoid a culture clash between analysts and IT which has previously led to six iterations of “AI winters?”
Enterprise architects must provide a clear conceptual framework for analytical capabilities and services for PAML solutions. The challenges are many; enterprise analytics often requires 99.999% availability, must be safeguarded from breaches and breakdowns, supports diverse user communities, and requires heterogeneous platforms. This talk will lay out the key characteristics of an architected analytics platform that rises to these challenges.
You will learn how to develop a scalable, flexible, and cost-effective data analytics platform, make the most of cloud computing, recognize the limits, and develop guidelines for the implementation of your analytics architecture.
You Will Learn
- The new enterprise paradigm: going beyond the data center
- Leveraging the cloud as an enterprise platform
- Tapping into Kubernetes for scalable environments
- Architectural frameworks and system connections for taming complexity
- Blueprints for enterprise deployment of advanced architectures
- Architecture case studies