Tag: ML

In this new exciting era of impressive AI advancements, Large Language Models (LLMs) like OpenAI’s ChatGPT have captured a huge interest from the public. Whether it’s used for writing emails, debugging code, or answering complex questions, ChatGPT shows an outstanding capabilit...

In this article, I will walk you through a complete Machine Learning Product Lifecycle and describe a Product Manager’s role in it. I do not define Product Management in too much detail. As a PM of AI-ML products, one needs to do everything that a PM does and, in addition to that, take care of...

With the monsoons here, slithering snakes are bound to fill the KGP campus along with the already present ‘Saanp’s’ in your class. So, why not get to know another ‘SNAKE’, which instead of hiding everything from you, helps you to not only break down past data but also p...

Recently, Hugging Face open sourced a heavyweight ML framework, Candle, which is a departure from the usual Python approach to machine learning, written in Rust with a focus on performance (including GPU support) and ease of use. According to Hugging Face, Candle’s core goal is to make...

When we train regressive models, we obtain point predictions. However, in practice we are often interested in estimating the uncertainty associated to each prediction. To achieve that, we assume that the value we are trying to predict is a random variable, and the goal is to estimate its distributio...

We talked about the current state of blockchain x AI integration, now begs the question…why? Does blockchain technology really need AI or vice versa? Empirically, there are a lot of projects developing interesting tangential use-cases involving putting AI personas or LLM-based consumer applic...

Pinterest’s mission is to bring everyone the inspiration to create a life they love. We rely on an extensive suite of AI powered products to connect over 460M users to hundreds of billions of Pins, resulting in hundreds of millions of ML inferences per second, hundreds of thousands of ML train...

Recently I’ve been spending a lot of time watching YouTube videos and have enrolled in more online courses than I can count, all in pursuit of grasping the basics of deep learning and machine learning. And you know what I’ve found? A lot of people use the terms Artificial Intelligence (A...

Generative AI (GenAI) and consequently large language models (LLMs) have gained significant traction and attention in the enterprise space this year. The hype promises transformative applications that are flexible to various industries and highly capable. As someone who has suppo...

The Machine Learning teams at Workday have a tremendous responsibility to develop reliable AI and ML. Building ever more trustworthy ML inferences is a path to either increase the value of our products (i.e., increased trust in the results) and to engage in conversations with customers. In this arti...

Hello, this is a blog where I share my personal life. I do not usually write about my personal life but I would like to experiment and see if people would be interested in reading about my life experiences. So, I hope this piece finds its readers and I hope you enjoy reading it. I studied and liv...

In today’s machine learning landscape, being an ML engineer means more than just building models. It’s about guiding a machine learning project from its inception to deployment and continuous improvement. While model creation is vital, the real power of ML engineering lies in seamlessly ...

When we train regressive models, we obtain point predictions. However, in practice we are often interested in estimating the uncertainty associated to each prediction. To achieve that, we assume that the value we are trying to predict is a random variable, and the goal is to estimate its distributio...

Artificial Intelligence and Machine Learning have become the centrepiece of strategic decision making for organizations. They are disrupting the way industries and roles function — from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edg...

A lot of repetitive boilerplate code exists in the model development phase of any machine learning application. Popular libraries such as PyTorch Lightning have been created to standardize the operations performed when training/evaluating neural networks, leading to much cleaner code. Howe...

As a data science practitioner, you may have found yourself scratching your head when trying to choose the best machine learning algorithm for your project. With so many options available, the process can be overwhelming and confusing. But fear not, because we’re here to simplify it for you. ...

Today, this is what I am selling: awesome_pipeline.fit(X, y) awesome_pipeline may look just like another variable, but here is what it does to poor X and y under the hood: Automatically isolates numerical and categorical features of X. Imputes missing va...

This article aims to examine the fundamental concepts of Ensemble Learning, a subset of machine learning methods that have gained widespread use worldwide. We will also introduce the common types of Ensemble Learning. However, due to the diverse nature of Ensemble Learning, we will break down the ...

This tutorial represents lesson 4 out of a 7-lesson course that will walk you step-by-step through how to design, implement, and deploy an ML system using MLOps good practices. During the course, you will build a production-ready model to forecast energy consumption levels f...

Hey there! I’m excited to talk to you today about a topic that might seem a bit unusual at first: using machine learning to predict divorce. That’s right, you heard me correctly. Thanks to the power of artificial intelligence and predictive modeling, we can now use data to make pre...

Learning by doing is one of the best approaches to learning anything, from tech to a new language or cooking a new dish. Once you have learned the basics of a field or an application, you can build on that knowledge by acting. Building models for various applications is the best way to make your kno...

Snowflake native apps allow you to develop applications directly on your data in your Snowflake cloud. With native apps, you can build out apps that can go above and beyond what traditional BI tools can do all while maintaining Snowflake’s high standard of security. Since native apps are still...

The AWS Machine Learning (ML) Specialty exam is often considered one of the most challenging AWS exams. It encompasses a wide range of skills in ML, AWS tools, and services. As a data engineer, I was eager to showcase my proficiency in AWS, machine learning, and data engineering. While there isn&rsq...

Logistic regression is a supervised machine learning algorithm that is used to predict the probability of an event occurring. It is a classification algorithm, which means that it can be used to classify data into two or more categories. Understanding Logistic Regression At its core, Logistic ...

The Machine Learning (ML) Lifecycle is a crucial framework that guides the development and deployment of machine learning models. It encompasses a series of interconnected stages. This article provides a glimpse into the various facets of the ML Lifecycle, industry standards, best practices and how ...

Neural networks when trained on some specific distribution of dataset will not work well when it will be tested on some different distribution. This is challenge which is faced by autonomous driving, network traffic management, autonomous drone navigation and in medical field. for example in case of...

This is an article on causal inference and decision making. There are two parts to this article. The first part introduces causal inference and explains why it is important for decision making. The second part focuses on how to apply causal inference to a real project. Here, I’ll present the f...