Running an Artificial Intelligence (AI) infrastructure on premise has major challenges like high capex and requires internal expertise. It can provide a lot of benefits for organisations that want to establish an AI strategy. The solution outlined in this post illustrates the power and the utility of the universal Operator Lifecycle Manager (OLM) using Juju, a universal OLM, to create and manage on premise AI infrastructure.

This article was previously published by Raconteur. Unmanageable workloads often have a knock-on effect on the customer experience (CX), and businesses can no longer afford to get this wrong. The expectation of an always-on experience and increasingly saturated markets can mean a poor experience will send customers elsewhere. And yet, many businesses are struggling to deliver the great customer experience that is demanded.

Artificial Intelligence is in the news a lot, and it’s hyped as a cure for all ills in the same breath it’s suspected of spelling doom for us all. What’s the truth behind all the noise? What does artificial intelligence do, seeing as it is simply everywhere. The truth of the matter is that whatever the would-be prophets say, artificial intelligence and machine learning is here, now, and has applications to your day-to-day.

With build-or-buy decisions, it often comes down to an all-in-one platform or a mixture of best-of-breed technologies. With open-source technology companies can actually get the best of everything. So, why not roll your own platform based on top-notch technologies? The real question is whether enterprises can afford to. Open-source software is free to use, but teams have to invest quite a bit in selecting, introducing, using, and maintaining these technologies.

AI is everywhere. Except in many enterprises. Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few models that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.

The last few years has been a time of major disruption in the Finserv sector. Artificial Intelligence (AI) technology has emerged as an important tool for providers of financial products and services to deliver more personalised and more sophisticated services to customers faster. The financial services sector is at the beginning of an exciting journey with AI – a journey that we believe will spark a revolution and redefine financial services.

The Splunk Deep Learning Toolkit (DLTK) is a very powerful tool that allows you to offload compute resources to external container environments. Additionally, you can use GPU or SPARK environments. In last Splunk blog post, The Power of Deep Learning Analytics and GPU Acceleration, you can learn more about building a GPU-based environment. Splunk DLTK supports Docker as well as Kubernetes and OpenShift as container environments.