EMA Blog: Business Intelligence and IT Management

Digital attackers in 2018 need to focus on one key challenge: “how do I deliver the best possible value to my clients, without significantly increasing cost.“

In 2017, CEOs arrived at the conclusion that machine learning and artificial intelligence (ML / AI) will be critical to unlock competitive advantages in the future. However, most enterprises had very little understanding of exactly what is possible today and how much value the investment in various ML / AI technologies can bring. Here are my six key recommendations for 2018:

Now that I’ve predicted that in 2018 machine learning will be available to ‘average Joe developer,’ let me share my experience from this weekend. Note that I’m not trying to be a ‘cool geek’ by doing some hands on work (I’m way too old to still be cool), but based on all the machine learning and artificial intelligence buzz in 2017, I thought my use case should be quick, simple, and most importantly solving a problem that could otherwise not be solved.

For readers who would like to see what happened to our 2017 predictions, please take a look. Jens Soeldner and the EMA Team sat down again this month to look into the crystal ball for 2018.

Here's the Sneak Peak to the EMA Container Management in Production and at Scale research project that so many of you have been asking about over the previous 2 months.

“We want everyday developers (...) to be able to use machine learning much more extensively.” This is Andy Jassy’s mantra targeted at making AWS the company that turns machine learning into a commodity, similar to what the company achieved for IaaS before. Within this context, the following two new offerings stood out of the glut of machine learning and IoT news at Re:Invent 2017.

Breaking the Triangle of Cost, Quality, and Speed

This year’s AWS Reinvent delivered major announcements in DevOps, machine learning and IoT. All of the announced capability aim to eliminate infrastructure as the bottleneck for enterprises to become ‘digital attackers’. Observing the nearly 50.000 developers, architects, and software operators that came to Reinvent showed us a significant degree of genuine excitement about Amazon helping enterprises release new software faster, at a higher quality and lower cost.

First Came Autonomic Computing
‘Autonomic Computing’ was the original concept of providing systems and apps with the power autonomously responding to unpredictable challenges. ‘Autonomic Computing’ came with all the right ideas (IBM deserves a good share of credit for defining this concept), but failed due to the same cultural and technological barriers DevOps is struggling with today. There simply was not enough ‘pressure to innovate.' This allowed inertia to prevail, leading to 'business as usual,' instead of magical self-healing and self-optimizing datacenter infrastructure.

This year’s DevOps Enterprise Summit (DOES) in San Francisco was carried by the enthusiasm of 1500 practitioners who were genuinely enthusiastic about how DevOps can transform their enterprises into a ‘digital attacker.’ Digital attackers rely as DevOps as their innovation engine to rapidly release high quality software that offers measurable business value. In short, digital attackers bully their competition by offering best in class customer value on an continuous basis and in a cost effective manner.

On October 25, Cisco and Google announced their hybrid cloud partnership, where Google brings the container runtime (Kubernetes), the platform to provide, manage, and consume APIs (Apigee), and of course a wide range of consumable cloud services (visual recogngition, machine learning, text to voice, etc.). Cisco contributes the hyperconverged infrastructure (Hyperflex) with Kubernetes management (Harmony), networking (Nexus 9k), and hybrid cloud management software (CloudCenter) to integrate Google’s public cloud services with the customer’s local data center.