When people think of AI hardware, NVIDIA often comes to mind due to its dominance in machine learning and deep learning applications. However, AMD—traditionally known for CPUs and gaming GPUs—has steadily been expanding its footprint in the AI domain. This leads to a common question among developers and businesses: Does AMD GPU use AI? The … Read more
When it comes to machine learning and deep learning, the GPU (Graphics Processing Unit) is often the heart of the system. For years, NVIDIA has dominated the AI GPU market with its CUDA ecosystem and top-tier performance. However, AMD has increasingly positioned itself as a competitive alternative, offering powerful GPUs with open-source software support and … Read more
The rise of deep learning and data-driven applications has brought a surge in demand for hardware acceleration, especially Graphics Processing Units (GPUs). However, CPUs (Central Processing Units) are still widely used in machine learning workflows—and for good reason. Despite the general preference for GPUs in training complex models, there are many scenarios where using a … Read more
If you’ve started exploring how neural networks are trained, you’ve likely come across the term “iteration.” Often used alongside words like “epoch” and “batch size,” iteration is one of the foundational concepts in machine learning training processes. But what does iteration actually mean in the context of a neural network, and why is it important? … Read more
In machine learning (ML), the pursuit of better accuracy is never-ending. With each new iteration, data scientists and engineers seek to improve predictions, reduce errors, and optimize outcomes. But how do you know that your new model version is actually better? That’s where A/B testing plays a critical role. A/B testing allows teams to compare … Read more
When deploying machine learning (ML) models into production, one of the biggest challenges is validating whether your new model version will actually outperform the existing one in real-world conditions. This is where A/B testing comes in. A/B testing is a controlled experimental technique that allows teams to compare two or more model variants by exposing … Read more
In the world of digital experimentation, A/B testing has long been a staple for making data-driven decisions. But what happens when you bring machine learning (ML) into the equation? The result is a powerful combination of experimentation and intelligent automation that allows organizations to optimize models, interfaces, and product features more efficiently. In this article, … Read more
Large Language Models (LLMs) like GPT-3, LLaMA, and Falcon have revolutionized the fields of NLP and generative AI. But working with these models requires substantial compute power, memory, and careful environment setup. Fortunately, Google Colab provides a free and convenient way to experiment with these models in a browser-based Jupyter environment. This article walks you … Read more
Machine Learning Operations, or MLOps, is a discipline that combines machine learning, DevOps, and data engineering to streamline and automate the end-to-end lifecycle of machine learning models. For beginners entering this field, the road ahead can seem overwhelming. This article offers a comprehensive MLOps road map tailored for beginners, guiding you through the key skills, … Read more
Machine Learning (ML) is transforming industries across the globe — from healthcare and finance to retail and entertainment. But building a machine learning model is just one part of the journey. Taking that model from development to production, ensuring it runs reliably, monitoring its performance, and managing it at scale — that’s where MLOps (Machine … Read more
