AI

This is the second post in the series where I translate AI into the language of infrastructure engineers. In the first post, I showed that AI is just another workload and that your infra skills already prepare you more than you think. Now for the bottleneck everyone ignores: storage. It is the hidden villain behind performance problems in almost every AI project I’ve seen. tl;dr Storage is usually the first bottleneck in AI training. Keep durable data in Blob or Data Lake, but cache the active working set on local NVMe. Check GPU, disk, and network metrics together before blaming the model or the GPUs. The midnight call You did everything right. The ML team asked for a GPU cluster and you delivered: eight NVIDIA A100s across two nodes, high-bandwidth networking, CUDA drivers up to date. Clean deployment. The team kicked off their first training job Friday at 6 PM and you went home feeling good. ...

This is the first post in a series where I’ll translate the world of AI into the language that infrastructure engineers already speak. If you’re the kind of professional who configures VMs, builds CI/CD pipelines, and gets woken up at 2 AM when Nagios fires, this content is for you. The series is based on my open-source book AI for Infrastructure Professionals, adapted and expanded here on the blog. tl;dr AI is another workload with different performance, cost, and data patterns. Infrastructure engineers already own the hard parts: compute, networking, security, observability, and cost control. If you can run production infrastructure well, you already have the foundation to support AI systems. The Monday morning message It’s 8:47 AM on a Monday. You’re halfway through your coffee, reviewing a Terraform plan for a network redesign, when a Slack message lights up your screen. It’s from the data science team lead: ...

As I embark on my journey of learning about artificial intelligence (AI), I am discovering the fascinating world of large language models (LLMs) and their applications in various technologies. In this article, I aim to share my newfound knowledge and insights with others who are also beginning their journey in AI. We will explore OpenAI, one of the leading organizations in AI research and development, and compare its offerings with Microsoft’s Azure OpenAI service. ...

As we continue our journey into artificial intelligence (AI), it’s important to understand how AI is transforming different industries and the ethical and legal challenges associated with its widespread adoption. In this new post, we will explore AI’s real-world applications and the complexities of ethical and legal concerns in detail. AI in Healthcare AI is making significant advances in healthcare, improving patient care and medical research: Diagnostics: AI-powered algorithms can analyze medical images, such as X-rays, MRIs, and CT scans, to identify diseases like cancer or fractures with high accuracy. These systems can serve as a second opinion for radiologists, improving diagnostic accuracy and efficiency. Personalized Medicine: AI enables the development of personalized treatment plans based on a patient’s unique genetic makeup. This approach can lead to more effective and targeted therapies, improving patient outcomes. Drug Discovery: AI accelerates the process of discovering new drugs by analyzing vast amounts of data to identify potential compounds and predict their efficacy. This reduces the time and cost associated with bringing new drugs to market. Remote Monitoring: AI-powered wearable devices and remote monitoring tools enable healthcare providers to track patients’ health in real-time, offering proactive care and reducing hospital readmissions. Administrative Efficiency: AI streamlines administrative tasks such as scheduling, billing, and insurance claims processing, freeing up healthcare professionals to focus on patient care. AI in Finance AI is reshaping the finance industry by providing innovative solutions to complex problems: ...