This article guides you through deploying a simple machine learning model for regression using Streamlit, a platform designed to streamline the deployment of machine learning systems as web services. Overview of the Deployment Process In this tutorial, we’ll focus on getting a machine learning model up and running in the cloud within minutes. We’ll create … Read more

The adoption of machine learning (ML) is accelerating, proving itself to be a potent tool for addressing various challenges. A great way to deepen your understanding of ML is through hands-on projects that provide real-world experience. Here are seven simple machine learning projects that will help you develop essential ML skills and enhance your career … Read more

Machine learning (ML) models are the byproducts of analyzing datasets to identify patterns, predict outcomes, or automate decision-making. While visualizing data is widely recognized in various stages of data science, visualizing ML models themselves is complex. It involves an understanding of their structure, performance, and behavior to inform decisions, often requiring specialized tools. Here are … Read more

Understanding the workings behind large language models (LLMs) is essential in today’s machine learning landscape. These models influence everything from search engines to customer service, making it important to grasp their fundamental concepts to unlock a world of opportunities. Here, we break down six critical concepts related to LLMs in a beginner-friendly manner, helping you … Read more

Artificial Intelligence (AI) research, particularly in the machine learning (ML) domain, continues to capture global attention. The volume of work in this field has surged, nearly doubling in submissions to the open-access pre-print archive ArXiv since late 2023, with over 30,000 AI-related papers available by the end of 2024. The majority of these are ML-focused, … Read more

Machine learning (ML) continues to be one of the most dynamic sectors in technology, influencing IT and various other industries. The field’s rapid growth has significantly transformed several areas, and as companies increasingly adopt AI-driven solutions, the demand for skilled ML professionals has surged. For those navigating a career in machine learning—whether aspiring or seasoned—it … Read more

Machine learning is at the forefront of recent technological advancements, significantly impacting areas like generative AI. Tools like ChatGPT, Perplexity, and Midjourney are widely used in daily tasks, showcasing how machine learning will continue to influence our work in the long term. As we close out 2024, numerous developments in the machine learning field make … Read more

Metrics are fundamental in evaluating any AI system, including large language models (LLMs). This article aims to clarify how popular evaluation metrics for language tasks performed by LLMs function internally, supplemented by Python code examples that illustrate their application using Hugging Face libraries. For a conceptual understanding of LLM metrics, we recommend additional readings that … Read more

With the rise of large language models (LLMs) like ChatGPT and Gemini, adapting our skills to meet current demands is essential. One crucial skill in today’s landscape is prompt engineering, which encompasses the art of designing prompts that optimize the performance of LLMs. By effectively structuring input prompts, we can elicit relevant and high-quality output … Read more

IntroductionTraining large language models (LLMs) involves a complex interplay of planning, computational resources, and domain knowledge. Whether you are a data scientist, machine learning practitioner, or AI engineer, it is easy to fall into common pitfalls during the training or fine-tuning of LLMs, which can adversely impact model performance and scalability. This article identifies five … Read more