Rapid cloud adoption has left many enterprises needing help with their technology infrastructure. These simple rules will keep the pain to a minimum.
There is widespread fear in the securities and finance sectors that using generative AI will force companies to rely on giant cloud companies.
CISOs are still hampered by bad assumptions and outdated approaches. They should be involved in decisions from day 1 to address unique business needs.
AI agents offer flexibility and autonomy as they plan and complete complex tasks that traditionally require human involvement.
The explosive growth of generative AI drives the multicloud model. But be prepared because it’s going to cost more money.
It's old news that no one in IT can find enough talent to build and run modern IT solutions. AI won’t save you, so start looking at other options.
Using edge systems to run elements of generative AI could be game-changing. It requires planning and skill, but this hybrid approach may be the future.
Leaving the cloud is not a matter of choosing between two clear-cut options. Few enterprises go completely data center or completely cloud.
The problems can be hard to find but easy to solve. With a proactive approach and best practices, you can avoid unhappy users and a damaged business reputation.
Many enterprises are dusting off the private cloud strategies that lost out to the allure of the public cloud. Is this the right move?
There’s definitely more uncertainty in going with a microcloud provider, but choosing a smaller company for your GPU services may pay off big in the end.
The idea of decentralizing cloud computing has been overshadowed by AI, but frustration with high cloud prices has boosted interest in other options.
It seems to be fair game now to label cloud security as risky even though your data is likely safer there than on premises.
Using the generic architecture you saw at a conference for your company's unique business needs is a surefire way to waste money and time.
New data reveals some interesting information about cloud cost management and the fear of being fired. Should we rethink our approaches?
Enterprises may find it faster and easier to deploy their AI models in a public cloud that runs them as a service. AWS is jumping on this trend.
With public cloud providers chasing generative AI, it may be a surprise when dollars flow in other directions. Vendors and customers have a lot to consider.
Let’s not make the same mistakes we did 10 years ago. It is possible to deploy large language models in the cloud more cost-effectively and with less risk.
A recent study shows that the cloud benefits the IT department more than other business areas. That’s not enough to make it a success.
ESG scores can be a helpful tool in the pursuit of sustainability. But they won’t look deep into your architecture to see if poor design is wasting money and energy.
It’s no surprise that AI will be a gold mine for cloud providers. However, if vendors and customers move too far in the wrong direction, we’ll waste business value for years.
Many systems architects already see too much focus on processors for generative AI systems and not enough attention on other vital components.
How much carbon is your software responsible for? Awareness of power consumption and accountability for it are the first steps in a green development cycle.
Interest in doing the right thing is extending to IT systems beyond AI. This is good, even if mainly motivated by the fear of legal or financial consequences.
A dirty little secret in the cloud world is that container workloads have a higher total cost of ownership than they should.
AI models that use data where it exists rather than centralizing it require stronger privacy and security measures. Introducing the RoPPFL framework.
Enterprise IT sees these fees as arbitrary and annoying, and cloud providers are taking notice. However, it’s not all about customer goodwill.
Generative AI systems for business are alarmingly inaccurate. Data needs some serious attention to avoid wrong info, bias, or legal trouble.
Cloud providers are becoming commoditized, so you have to be careful to determine where the best value lies.
Let’s clear up the confusion around the semantics of these critical roles. They offer a combo of strategic vision and on-the-ground development skills.
Cloud can be a green technology, but not without significant planning and up-front work that most enterprises are reluctant to fund.
Recent reports supply old and new information about finops. Financial priorities are changing, and more employee training is needed.
It’s clear that AI, including generative AI, will be tested in the courts. Cloud and AI architects must practice defensive design and governance to stay out of trouble.
Avoid the stereotype that hybrid cloud is inefficient by implementing measurable objectives, customized architecture, and continuous testing and monitoring.
Not so fast. Mainframes have more staying power than most understand. Let’s look at the realities of mainframe technology and the people who operate it.
Natural language generation, recommendation systems, and anomaly detection are good opportunities to create strong business value with genAI.
Cloud is a good fit for modern applications, but most enterprise workloads aren’t exactly modern. Security problems and unmet expectations are sending companies packing.
A lot of different skills are needed to create a system that can do the most for your business. Here’s a description of the important roles.
Without a magic bullet, enterprises need to do the hard work of optimizing their cloud workloads, especially before jumping into generative AI.
Without basic computer architecture best practices, generative AI systems are sluggish. Here are a few tips to optimize complex systems.
We’re getting too much latency from poorly designed, developed, and deployed cloud-based APIs and services. It’s time to brush up on testing and monitoring.
The verdict is in. Most cloud computing failures can be traced back to very human mistakes. What (expensive) lessons have we learned?
Cloud providers are stacking up 'junk fees' and enterprises are pushing back. Here are a few tips to better negotiate and manage those fees.
There is so much interest in GPUs for generative AI deployment and for some good reasons. However, in some cases, they are overkill and too expensive.
Finops offers obvious financial benefits, but security may be its secret weapon. It's time to have the finops and security teams combine their efforts.