Category Archives: AI
How Indexing Works in LLM-Based RAG Applications
When building a Retrieval-Augmented Generation (RAG) application powered by Large Language Models (LLMs), which combine the ability to generate human-like text with advanced retrieval mechanisms for precise and contextually relevant information, effective indexing plays a pivotal role. It ensures that only the most contextually relevant data is retrieved and fed into the LLM, improving the quality and accuracy of the generated responses. This process reduces noise, optimizes token usage, and directly impacts the application’s ability to handle large datasets efficiently. RAG applications combine the generative capabilities of LLMs with information retrieval, making them ideal for tasks such as question-answering, summarization, or domain-specific problem-solving. This blog will walk you through the …
Top 10 Gartner Technology Trends for 2025
What revolutionary technologies and industries will define the future of business in 2025? As we approach this pivotal year, the technological landscape continues to evolve rapidly, reshaping industries and redefining business strategies. Gartner has unveiled its top 10 strategic technology trends for 2025, emphasizing advancements in AI, computing, and human-machine interaction. Here’s a closer look at these transformative trends: Agentic AI Agentic AI systems are designed to autonomously plan and execute tasks based on user-defined goals. These virtual assistants are poised to enhance productivity by automating decision-making processes. By 2028, Gartner predicts that at least 15% of daily work decisions will be autonomously handled by agentic AI, a significant leap …
Agentic Reasoning Design Patterns in AI: Examples
In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents, such as virtual assistants, autonomous robots, and conversational large language models (LLMs) agents. These agents can think, act, and collaborate to achieve complex goals. Agentic Reasoning Design Patterns help explain how these agents work by outlining the essential strategies that AI agents use for reasoning, decision-making, and interacting with their environment. What is an AI Agent? An AI agent, particularly in the context of LLM agents, is an autonomous software entity capable of perceiving its environment, making decisions, and taking actions to achieve specific goals. LLMs enable these agents to understand natural language and reason …
Random Forest Classifier – Sklearn Python Example
Last updated: 14th Aug, 2024 A random forest classifier is an ensemble machine learning model which is used for classification problems, and operates by constructing a multitude of decision trees during training, and, predicting the class label (of the data). In general, Random Forest is popular due to its high accuracy, robustness to overfitting, ability to handle large datasets with numerous features, and its effectiveness for both classification and regression tasks. Random Forest and Decision Tree classification algorithms are different, although Random Forest is built upon the concept of Decision Trees. In this post, you will learn about the concepts of random forest classifiers and how to train a Random …
LLM Hosting Strategy, Options & Cost: Examples
As part of laying down application architecture for LLM applications, one key focus area is LLM deployments. Related to LLM deployment is laying down LLM hosting strategy as part of which different hosting options need to be looked at, and evaluated based on various criteria including cost and appropriate hosting should be selected. In this blog, we will learn about different hosting options for different kinds of LLM and related strategies. LLM Hosting Cost depends on the type of LLM Needed What is going to be the cost related to LLM hosting depends upon the type of LLM we need for our application. LLM Hosting Cost for Proprietary Models If …
AI-Ready Data Explained with Examples
AI-ready data usually refers to data that has been prepared in such a way that it can be effectively used for training artificial intelligence (AI) and generative AI models. In this blog, we will learn about what are the most common attributes of AI-ready data. The following are the top most 5 attributes that AI-ready data would need to have. Data must be: Check out this Gartner paper for further details – We Shape AI, AI shapes us.
AI-assisted Software Development: Tools & Processes
In the rapidly evolving landscape of software development, the integration of artificial intelligence (AI) and generative AI (Gen AI) is not just a luxury but a cornerstone for enhancing software development velocity. This blog delves into the key aspects of Gen AI and AI-assisted software development, presenting actionable takeaways for software leaders, including engineering managers, project managers, product managers, and software engineers. We will look into different tools and related processes that can be enhanced across the entire software development lifecycle. Design & Architect: Crafting the Blueprint Integrate the following tools to speed up the design process while ensuring adherence to best practices, significantly reducing design iteration times. Code & …
Machine Learning Models Solution Design: Examples
This blog is crafted for data scientists, machine learning (ML) and software engineers, business analysts / product managers, and anyone involved in the ML project lifecycle, aiming to create a reliable solution design and development strategy / plan for successful AI / machine learning project implementation and value realization. The blog revolves around a series of critical solution design questions, meticulously curated to guide teams from the initial conception of a project to its final deployment and beyond. By addressing each of these solution design questions, teams can ensure that they are not only building a model that is technically proficient but also one that aligns seamlessly with business objectives, …
Data Science Competitions on Different Online Platforms
Data science / Machine Learning is an ever-evolving field, and competitions provide a great way for beginners / practitioners to hone their skills, solve real-world problems, enhance their resumes / CVs and even earn rewards. Here’s a roundup of some notable machine learning / data science / AI competition platforms, each offering unique opportunities. Each of these data science competition platforms offers unique opportunities and challenges, making them ideal for both beginners and expert data scientists at various stages of their careers to learn, compete, and contribute to a wide array of problems.
Introducing Our New Data Science & AI Trends Page
We are thrilled to announce the launch of our dedicated Data Science and AI Trends page at VitalFlux.com! This new resource is designed to be a one-stop hub for data scientists, AI enthusiasts, and anyone passionate about staying at the forefront of technological innovation. What You’ll Find Our Data Science & AI Trends page is more than just a collection of articles; it’s a dynamic resource that aggregates the most insightful and current information from various high-impact sources. Here’s a sneak peek at what you can expect: Web Pages Stay informed with our selection of web pages from leading research institutions, tech news outlets, and individual thought leaders in the …
Linear Regression in Machine Learning: Python Examples
Last updated: 15th Dec, 2023 In this post, the linear regression concepts in machine learning is explained with multiple real-life examples. Two types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. In addition, Python code examples are used for demonstrating training of simple linear and multiple linear regression models. In case you are a machine learning or data science beginner, you may find this post helpful enough. You may also want to check a detailed post – What is Machine Learning? Concepts & Examples. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models …
Feature Scaling in Machine Learning: Python Examples
While training machine learning models, we come across the need for scaling features in order to have different features contribute to the predictions in an appropriate manner. Without scaling, features with larger numerical ranges can dominate those with smaller ranges, leading to biased or inefficient learning. In this post you will learn about this feature engineering technique namely feature scaling with Python code examples using which you could significantly improve performance of machine learning models. To demonstrate the technique, the models will be trained using Perceptron (single-layer neural network) classifier. What is Feature Scaling? Why is it needed? Feature scaling is a method used to standardize the range of independent variables …
Confusion Matrix Concepts, Python Code Examples
The confusion matrix is an essential tool in the field of machine learning and statistics for evaluating the performance of a classification model. It’s particularly useful when dealing with binary or multi-class classification problems. In this post, you will learn about the confusion matrix with examples and how it could be used as performance metrics for classification models in machine learning. What is Confusion Matrix? A confusion matrix is a table used to describe the performance of a classification model on a set of test data for which the true values are known. It’s most useful when you need to know more about the accuracy of the model than just …
R-squared in Linear Regression Models: Concepts, Examples
In linear regression, R-squared (R2) is a measure of how close the data points are to the fitted line. It is also known as the coefficient of determination. Understanding the concept of R-squared is crucial for data scientists as it helps in evaluating the goodness of fit in linear regression models, compare the explanatory power of different models on the same dataset and communicate the performance of their models to stakeholders. In this post, you will learn about the concept of R-Squared in relation to assessing the performance of multilinear regression machine learning model with the help of some real-world examples explained in a simple manner. Before doing a deep dive, …
Microsoft’s Free Courses: Data Science, Machine Learning, AI
Are you keen on diving into the world of data science, machine learning, or artificial intelligence? Have you been searching for courses that not only teach the fundamentals but are also free and accessible? Look no further! Microsoft has put together three distinct courses that will cater to your interests and ignite your passion for learning. Data Science for Beginners This course offers an ideal starting point for those new to data science, focusing on the basics and guiding through practical exercises. The course would help you demystify the complex world of data, allowing you to make informed decisions in various fields such as business, healthcare, and more. Each lesson …
Hypothesis Testing Steps & Examples
Hypothesis testing is a technique that helps scientists, researchers, or for that matter, anyone test the validity of their claims or hypotheses about real-world or real-life events in order to establish new knowledge. Hypothesis testing techniques are often used in statistics and data science to analyze whether the claims about the occurrence of the events are true, whether the results returned by performance metrics of machine learning models are representative of the models or they happened by chance. This blog post will cover some of the key statistical concepts including steps and examples in relation to what is hypothesis testing, how to formulate them and how to use them in …
I found it very helpful. However the differences are not too understandable for me