Why Machine Learning Diagnoses Cancer but Can’t Run our Hybrid Cloud


Why Machine Learning Diagnoses Cancer but Can’t Run our Hybrid Cloud Why can machine learning and artificial intelligence reliably find cancer, for example, in CT scans (read the excellent very short MIT article), but they cannot tell me whether or not it makes sense to containerize an application, how to most efficiently provision storage pools [...]



6 Recommendations for Machine Learning and Artificial Intelligence in 2018


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 [...]



By | December 26th, 2017|Artificial Intelligence, Machine Learning|0 Comments

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


“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 [...]



By | December 4th, 2017|Artificial Intelligence, Machine Learning|0 Comments

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


“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, [...]



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


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 [...]



By | September 26th, 2017|Artificial Intelligence, Containers, DevOps, Megatrends|0 Comments

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


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? When I talk to the data center guys, [...]



The Four Horsemen of the Hybrid Cloud Apocalypse – Vision of the Business Defined Data Center

The 4 key risks (horsemen) of hybrid cloud

EMA estimates that enterprises today waste over 50% of their IT budgets on inefficient application workload placement, configuration and management. As a side effect, they introduce tremendous operational risk in terms of security, regulatory compliance, performance and reliability. EMA's 2017 hybrid cloud, software-defined data center (SDDC) and machine learning-related research projects (take a look at [...]



Machine Learning and Artificial Intelligence for the Masses


Recently, I’ve been thinking about why we haven’t come all that far in machine learning and artificial intelligence over the previous decade. Today, Keen Browne, Bonsai’s Co-founder and Head of Product, summed it up for me in a concise manner: “The tools really suck and are meant for scientists and mathematicians, not for people who [...]



Load More Posts