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.
Research agrees: Most businesses need help with cloud-focused digital transformation efforts. Luckily, most wounds are self-inflicted and easily avoided.
Survey after survey will tell you what most hiring managers already know. You can find good cloud architects, but it will cost you.
Just when you thought you knew all the ways hackers could access your data on a public cloud, a new threat has emerged. Luckily, the fixes are manageable.
All the worry over technical debt and the risks of cloud vendor lock-in has spread to AI, only the stakes are now much higher.
The mantra for computing is to use other people’s technology, so it's surprising that generative AI doesn’t seem to follow that pattern for many enterprises.
A new study shows that AI won’t work without a lot more talent in the employment market. Enterprises will need to be innovative to win this one.
I’m having ‘Sixth Sense’ moments when I see dead databases walking. With GenAI poised to eat your data for lunch, it’s time to fix performance problems.
With the rising costs of the cloud and the availability of connected computing and storage resources, we could see colossal cloud brands that don’t own data centers.
We used to be a lot more interested in the mechanics of multitenancy. What are the exciting evolutions in the technologies that support it?
The mega cloud conferences hosted by cloud providers never fail to mesmerize us with shiny new technology. We should focus on our business needs instead.
A recent study shows a narrowing gap between enterprises' application development skills and the likelihood of a vast data breach.
Most training covers cloud brand-related skills. However, if we’re looking for talent to scale, we must go broad rather than deep.
Predictions are the ability to see the continuation of existing patterns. Yes, genAI is on the list, as well as the talent crunch and the partnership between business and IT.
KubeCon + CloudNativeCon just concluded with another land grab for generative AI. What does this mean for the enterprise?
Forty years ago, AI was largely shelved because of its high price tag. By finding the real business benefits, you can do better than the developers of yesterday.
The higher the cloud bills, the more questions get asked. Here’s how to evaluate if you should divorce your cloud provider.
There’s a lot of talk but not many actual implementations of generative AI in the cloud. Better to have all the pieces in place before launching expensive projects.
It’s time to look at top priorities for cloud deployments. It's a great opportunity to tackle access controls, cost optimization, and complexity.
Cloud computing and IT are starting to prefer experience to an expensive college education. What’s the best hiring strategy in this new normal?
What do you do now? How do you keep your job? There are a few things that could save the project and your career.
Edge computing offers less latency and bandwidth savings, but the lack of standards and problems with interoperability and security still need to improve.
From system design to daily performance tuning, here's a checklist of ways to make your systems run effectively.
GenAI can analyze application dependencies, network configurations, and security risks, but it will mostly help lazy companies that aren't doing this anyway.
In addition to integration and intellectual property challenges, companies may not have the technical expertise to customize or secure open source software.
The cloud is integral to most business operations and spending remains unaffected by lower corporate revenue. Still, let's make the most of your cloud dollars.
AI-based design and development is exciting but it doesn't replace sound, solid architecture and engineering in building and deploying cloud-based solutions.
Enterprises want generative AI, but CIOs need a way to pay for it. Diverting spending from traditional cloud computing may not be the best strategy.
The time is coming when poor IT design and decisions will be outed by finops automation and artificial intelligence. Are you ready to defend yourself?
2023 might be the year of repatriation, but more challenging architectural decisions need to be made besides what saves a few cloud dollars.
These prebuilt components simplify development and offer flexibility and speed, but watch out for scalability, security, and integration problems.
You might think that running back to get a master's degree or joining a country club to make business contacts is the best strategy. It’s simpler than that.
Enterprises facing high cloud costs are taking a more balanced look at where workloads should reside and considering repatriation to a cloud in their own data center.