Browsing: Business & Startups

Image by Author   # Introduction  Developers use pandas for data manipulation, but it can be slow, especially with large datasets. Because of this, many are looking for faster and lighter alternatives. These options keep the core features needed for analysis while focusing on speed, lower memory use, and simplicity. In this article, we look at five lightweight alternatives to pandas you can try.   # 1. DuckDB  DuckDB is like SQLite for analytics. You can run SQL queries directly on comma-separated values (CSV) files. It is useful if you know SQL or work with machine learning pipelines. Install it with:  …

Large Language Models (LLMs) are the heart of Agentic systems and RAG systems. And building with LLMs is exciting until the scale makes them expensive. There is always a tradeoff for cost vs quality, but in this article we will explore the 10 best ways according to me that can slash costs for the LLM usage while focusing on maintaining the quality of the system. Also note I’ll be using OpenAI API for the inference but the techniques could be applied to other model providers as well. So without any further ado let’s understand the cost equation and see ways…

Artificial intelligence and diabetes: two topics that are very close to my heart. That’s why, in association with World Diabetes Day, I’ve decided to show you the many fascinating ways AI is helping the medical world gain ground in the fight against the disease. Whether you have diabetes or not: I have no doubt you’ll enjoy humankind’s ability to innovate. Recognizing World Diabetes Day On Saturday, November 14th, the world’s focus turned to World Diabetes Day: an annual global campaign raising awareness of diabetes. The International Diabetology Federation established the campaign in 1991. And they chose November 14th as it’s…

Image by Author   # Introduction  I’ve worked in the data industry for over four years. During this time, I’ve witnessed a seismic shift in the role. Previously, when screening candidates for data analyst positions, it was easier to tell who possessed the technical skill to do the job and who didn’t. Nowadays, almost everyone gets through the first few rounds of the data analyst interview. With AI, candidates who have very little practice are building dashboards and writing SQL queries with precision — skills that once took years to learn. As a result, employer expectations have changed, with seniority and…

I’ve spent plenty of time building agentic systems. Our platform, Mentornaut, already runs on a multi-agent setup with vector stores, knowledge graphs, and user-memory features, so I thought I had the basics down. Out of curiosity, I checked out the whitepapers from Kaggle’s Agents Intensive, and they caught me off guard. The material is clear, practical, and focused on the real challenges of production systems. Instead of toy demos, it digs into the question that actually matters: how do you build agents that function reliably in messy, unpredictable environments? That level of rigor pulled me in, and here’s my take…

We’ve written in-depth about the differences between AI, Machine Learning, Big Data, and Data Science. Today, it’s time to explore another term that holds equal weight in the modern business world: Data Mining. In this article, you’ll learn what data mining is, the steps involved, the different models used, and most importantly, what you can achieve by using data mining solutions in your industry — without further ado, let’s begin. What Is Data Mining? Data mining involves searching vast volumes of data for patterns and trends. And the practice can answer questions that a simple query-and-report process cannot. Data mining…

Image by Author   # Introduction  Standard Python objects store attributes in instance dictionaries. They are not hashable unless you implement hashing manually, and they compare all attributes by default. This default behavior is sensible but not optimized for applications that create many instances or need objects as cache keys. Data classes address these limitations through configuration rather than custom code. You can use parameters to change how instances behave and how much memory they use. Field-level settings also allow you to exclude attributes from comparisons, define safe defaults for mutable values, or control how initialization works. This article focuses on…

Deep learning models are based on activation functions that provide non-linearity and enable networks to learn complicated patterns. This article will discuss the Softplus activation function, what it is, and how it can be used in PyTorch. Softplus can be said to be a smooth form of the popular ReLU activation, that mitigates the drawbacks of ReLU but introduces its own drawbacks. We will discuss what Softplus is, its mathematical formula, its comparison with ReLU, what its advantages and limitations are and take a stroll through some PyTorch code utilizing it. What is Softplus Activation Function?  Softplus activation function is…

In my previous article, I compared the performance of Python to JavaScript when training a machine learning model. Though JavaScript should have had a computational advantage, it didn’t stand a chance against Python. Data processing is Python’s strong suit. In contrast, as illustrated by my study, JavaScript can only handle smaller datasets. Truth be told, Python outperforms JavaScript on many fronts when it comes to machine learning, especially in terms of the availability and maturity of ML libraries. That said, despite JavaScript’s apparent shortcomings, many new libraries are emerging — while existing ones are gathering an ever-growing developer community. And…

Image by Author   # Introduction  You might have trained countless machine learning models at university or on the job, but have you ever deployed one so that anyone can use it through an API or a web app? Deployment is where models become products, and it’s one of the most valuable (and underrated) skills in modern ML. In this article, we will explore 10 GitHub repositories to master machine learning deployment. These community-driven projects, examples, courses, and curated resource lists will help you learn how to package models, expose them via APIs, deploy them to the cloud, and build real-world…