Level: Beginner to Intermediate
Predictive analytics and machine learning (PAML) is 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”? How can you enable capabilities such as edge analytics and interactive visualizations?
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, safeguards against breaches and breakdowns, support for diverse user communities, and heterogeneous platforms. Above and beyond this, the platform must be able to accommodate every type of user and their requests, whether dataset or analytics or interactive visualization. This session will lay out the key characteristics of an architected analytics platform that will rise 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 its limits, and develop guidelines for the implementation of your analytics architecture.
You Will Learn
- The new enterprise paradigm: going to the cloud data center
- How to tap into Kubernetes for scalable environments and orchestration
- Architectural frameworks and system interfaces for taming complexity
- Blueprints for enterprise deployment of advanced architectures
- Architecture case studies
- Enterprise architects
- System API designers
- Full stack engineers
- Product managers