Many educational institutions maintain their own data centers. “We need to minimize the amount of work we do to keep systems up and running, and spend more energy innovating on things that matter to people.”
what’s the difference between machine learning (ML) and artificial intelligence (AI)?
Jeff Olson: That’s actually the setup for a joke going around the data science community. The punchline? If it’s written in Python or R, it’s machine learning. If it’s written in PowerPoint, it’s AI.
machine learning is in practical use in a lot of places, whereas AI conjures up all these fantastic thoughts in people.
What is serverless architecture, and why are you excited about it?
Instead of having a machine running all the time, you just run the code necessary to do what you want—there is no persisting server or container. There is only this fleeting moment when the code is being executed. It’s called Function as a Service, and AWS pioneered it with a service called AWS Lambda. It allows an organization to scale up without planning ahead.
How do you think machine learning and Function as a Service will impact higher education in general?
The radical nature of this innovation will make a lot of systems that were built five or 10 years ago obsolete. Once an organization comes to grips with Function as a Service (FaaS) as a concept, it’s a pretty simple step for that institution to stop doing its own plumbing. FaaS will help accelerate innovation in education because of the API economy.
If the campus IT department will no longer be taking care of the plumbing, what will its role be?
I think IT will be curating the inter-operation of services, some developed locally but most purchased from the API economy.
As a result, you write far less code and have fewer security risks, so you can innovate faster. A succinct machine-learning algorithm with fewer than 500 lines of code can now replace an application that might have required millions of lines of code. Second, it scales. If you happen to have a gigantic spike in traffic, it deals with it effortlessly. If you have very little traffic, you incur a negligible cost.
After Kubernetes,Istio is the most popular cloud-native technology. It is a service mesh that securely connects multiple microservices of an application. Think of Istio as an internal and external load balancer with a policy-driven firewall with support for comprehensive metrics. The reason why developers and operators love Istio is the non-intrusive deployment pattern. Almost any Kubernetes service can be seamlessly integrated with Istio without explicit code or configuration changes.
Google recently announced a managed Istio service on GCP. Apart from Google, IBM, Pivotal, Red Hat, Tigera and Weaveworks are the active contributors and supporters of the project.
Istio presents an excellent opportunity for ISVs to deliver custom solutions and tools to enterprises. This project is bound to become one of the core building blocks of cloud-native platforms. I expect every managed Kubernetes service to have a hosted Istio service.
Prometheus is a cloud-native monitoring tool for workloads deployed on Kubernetes. It plugs a critical gap that exists in the cloud-native world through comprehensive metrics and rich dashboards.
If Kubernetes is the new OS, Helm is the application installer. Designed on the lines of Debian packages and Red Hat Linux RPMs, Helm brings the ease and power of deploying cloud-native workloads with a single command.
One of the promises of cloud-native technology is the rapid delivery of software. Spinnaker, an open source project initially built at Netflix delivers that promise. It is a release management tool that adds velocity to deploying cloud-native applications.
Event-driven computing is becoming an integral part of modern application architecture. Functions as a Service (FaaS) is one of the delivery models of serverless computing which complements containers through event-based invocation. Modern applications will have services packaged as containers and functions running within the same environment.