While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads. In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, discussed how data centers of the future will be managed, how the public cloud best suits your organization, and what the future holds for operations and infrastructure engineers in a post-container world. Is a serverless world inevitable?

The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.

Wooed by the promise of faster innovation, lower TCO, and greater agility, businesses of every shape and size have embraced the cloud at every layer of the IT stack – from apps to file sharing to infrastructure. The typical organization currently uses more than a dozen sanctioned cloud apps and will shift more than half of all workloads to the cloud by 2018. Such cloud investments have delivered measurable benefits. But they’ve also resulted in some unintended side-effects: complexity and risk. End users now struggle to navigate multiple environments with varying degrees of performance. Companies are unclear on the security of their data and network access. And IT squads are overwhelmed trying to monitor and manage it all.

Machine learning provides predictive models which a business can apply in countless ways to better understand its customers and operations. Since machine learning was first developed with flat, tabular data in mind, it is still not widely understood: when does it make sense to use graph databases and machine learning in combination? This talk tackles the question from two ends: classifying predictive analytics methods and assessing graph database attributes. It also examines the ongoing lifecycle for machine learning in production. From this analysis it builds a framework for seeing where machine learning on a graph can be advantageous.'

With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.