Deploying Large Language Models (LLMs) for inference is a complex yet rewarding process that requires balancing performance, cost, and scalability. Optimizing and sizing LLM inference systems involves understanding tradeoffs, selecting the right tools, and leveraging NVIDIA’s advanced technologies like TensorRT-LLM, Triton Inference Server, and NVIDIA Inference Microservices (NIM). This guide explores the key techniques and strategies for efficient LLM deployment.

Can we teach children good values, like sustainability, through coding? In an increasingly digital world, coding has emerged as a critical skill for the future. It’s often touted not just as a technical tool but as a means to teach children broader life skills, such as problem-solving, creativity, and collaboration. Some educators and researchers even argue that coding can be used to instill good values like sustainability, empathy, or social responsibility. While the idea is promising and has its merits, there are significant challenges that suggest coding may not be the ideal medium for instilling such values.

In today’s fast-paced and competitive world, businesses must evolve to stay ahead. There is a profound difference between organizations that focus solely on production or engineering and those that embrace a market- and service-oriented mindset. To truly thrive, companies must find the perfect balance—leveraging their technical strengths while transforming into customer-focused enterprises. This journey is not just about survival; it’s about unlocking new opportunities, creating value, and building a legacy of excellence.

Every day, I would take an hour to retreat to my study — a quiet space where I could think, learn, and create. During this time, I would read books, listen to podcast, write down my thoughts in blog, and even look up things I didn’t know. These moments of peace and focus were the most rewarding part of my day. They reminded me that success doesn’t just come from working hard—it comes from finding balance. A sharp mind and a kind heart are equally important in life and work.

In today’s fast-paced and hyper-competitive world, innovation is the lifeblood of progress. Companies and individuals alike must continuously push boundaries to stay ahead. The future of innovation lies not just in groundbreaking technologies but in fostering a mindset that embraces boldness, creativity, and action. Innovation is not a one-time achievement; it is a continuous process that demands vigilance, adaptability, and courage. In industries where technology evolves at breakneck speed, standing still is not an option. Organizations that succeed are those that consistently reinvent themselves, adapt to emerging trends, and invest in long-term growth. Embracing emerging technologies, committing to sustainability, and adopting a global perspective are key drivers of sustained innovation. Companies and individuals must remain vigilant, continuously learn, and adapt to stay relevant in an ever-changing world.

Amazon SageMaker is a fully managed machine learning (ML) service from AWS that enables data scientists and developers to build, train, and deploy ML models quickly and efficiently. It eliminates the heavy lifting involved in setting up infrastructure, allowing organizations to focus on innovation. With a suite of tools for data preparation, model development, and governance, SageMaker provides an end-to-end ML workflow tailored for scalability and ease of use.

Generative AI has rapidly become one of the most transformative technologies of our time, enabling businesses to automate content creation, enhance customer experiences, and drive efficiency at scale. However, building and deploying AI-powered applications traditionally required extensive expertise, computational resources, and data governance strategies.

The cyclical nature of economic booms and busts is well-documented throughout history. Despite repeated lessons, markets continue to experience periods of irrational exuberance followed by painful corrections. At the heart of this cycle is a fundamental human tendency—our proclivity to follow trends without critical evaluation.

As organizations scale their cloud infrastructure, the volume of logs, metrics, and traces generated grows exponentially. Observability platforms help manage this data efficiently, providing insights into system performance, security, and troubleshooting. Among the leading solutions are Coralogix, Dynatrace, and the ELK Stack (Elasticsearch, Logstash, Kibana), each offering unique features suited to different use cases.

Terragrunt is a thin wrapper for Terraform that helps manage and reduce complexity in infrastructure-as-code (IaC) workflows. It streamlines Terraform configurations by promoting DRY (Don’t Repeat Yourself) principles, enforcing best practices, and handling remote state management more efficiently.