The Frustrations of Applied Machine Learning – A Not So Common Case Study

By Torsten Volk on Dec 17, 2017 1:33:54 PM

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.

Continue Reading

Top 10 enterprise IT Predictions for 2018 – Release Faster, Cheaper, and at Higher Quality – Everything Is about Becoming a Digital Attacker

By Torsten Volk on Dec 10, 2017 2:26:13 PM

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.

Continue Reading

AWS Re:Invent 2017 – Two Big Time Machine Learning Highlights

By Torsten Volk on Dec 4, 2017 9:33:31 AM

“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.

Continue Reading

Intent Driven DevOps – A New Journey to NoOps

By Torsten Volk on Nov 27, 2017 6:07:38 PM

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.

Continue Reading

Top 3 Guidelines for Leveraging Machine Learning and Artificial Intelligence to Lower OPEX and Increase Competitiveness

By Torsten Volk on Oct 2, 2017 11:40:35 AM

“Machine learning (ML) today is frustrating. There is so much potential and the algorithms are all there, but I just do not know how I can leverage it for my organization,” says the CTO of a major professional services firm. “My CEO wants me to ‘leverage ML to lower OPEX and differentiate our service offerings, but there is nothing out there in the market that would allow me to get this done in a manner that has a high probability of success.” Then of course he asks me what I would do and where I would start, because the guy with “Machine Learning and AI” in his job title must know for sure…

Continue Reading

Machine Learning and Artificial Intelligence: The Promised Land for Lowering IT OPEX, Decreasing Operational Risk and Optimally Supporting Business Goals

By Torsten Volk on Sep 26, 2017 11:37:40 AM

What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'

Continue Reading

Machine Learning and Artificial Intelligence: The Promised Land for Lowering IT OPEX, Decreasing Operational Risk and Optimally Supporting Business Goals

By Torsten Volk on Sep 26, 2017 10:44:15 AM

What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'

Continue Reading

Prologue for EMA Research Project: Machine Learning and Artificial Intelligence in Enterprise IT and DevOps

By Torsten Volk on Sep 18, 2017 2:29:07 PM

Why are there still so many repetitive tasks in data center and cloud management today? Why does application management still contain so many manual steps? Why do most organization still suffer from automation and monitoring silos that prevent them from avoiding preventable application outages and service degradations?

Continue Reading

VMworld 2017 – Prologue

By Torsten Volk on Aug 18, 2017 4:18:34 PM

“Oh, I’m not coming to VMworld this year, we are at Jenkins instead.” This is a sentence I’ve heard quite a few times now from vendors that I’m used to seeing at VMworld and it makes me wonder what’s going on.

Continue Reading

EMA Top 3 Product Selection Process

By Torsten Volk on Jun 16, 2017 5:14:24 PM

(You can read more about the content of the EMA Top 3 - Enterprise Decision Guide in my first Post)

Continue Reading

    Lists by Topic

    see all

    Posts by Topic

    see all

    Recent Posts