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The fields of Data Science, Artificial Intelligence (AI), and Large Language Models (LLMs) continue to evolve at an unprecedented pace. To keep up with these rapid developments, it’s crucial to stay informed through reliable and insightful sources. In this blog, we will explore the top 7 LLM, data science, and AI blogs of 2024 that have been instrumental in disseminating detailed and updated information in these dynamic fields.
In the rapidly evolving world of artificial intelligence, Large Language Models (LLMs) have become pivotal in transforming how machines understand and generate human language. To ensure these models are both effective and responsible, LLM benchmarks play a crucial role in evaluating their capabilities and limitations. This blog delves into the significance of popular benchmarks for LLM and explores some of the most influential LLM benchmarks shaping the future of AI.
Imagine a world where bustling office spaces fell silent, and the daily commute became a distant memory. When COVID-19 hit, that world became a reality, transforming how we work. Remote work quickly transitioned from a perk to a necessity, and data science—already digital at heart—was poised for this change. According to a recent report from Gartner, 47% of employers are open to full-time remote work even beyond the pandemic, highlighting a massive shift in the job landscape.
Generative AI research is rapidly transforming the landscape of artificial intelligence, driving innovation in large language models, AI agents, and multimodal systems. Staying current with the latest breakthroughs is essential for data scientists, AI engineers, and researchers who want to leverage the full potential of generative AI. In this comprehensive roundup, we highlight this week’s top 4 research papers in generative AI research, each representing a significant leap in technical sophist
Artificial intelligence (AI) has transformed industries, but its large and complex models often require significant computational resources. Traditionally, AI models have relied on cloud-based infrastructure, but this approach often comes with challenges such as latency, privacy concerns, and reliance on a stable internet connection. Enter Edge AI, a revolutionary shift that brings AI computations directly to devices like smartphones, IoT gadgets, and embedded systems.
In many enterprise scenarios, SharePoint-hosted Excel files serve as the bridge between raw data and business operations. But keeping them up to date, especially when your data lives in Azure Synapse , can be surprisingly difficult due to limitations in native connectors. In this guide, you’ll learn a step-by-step method to build a no-code/low-code Azure Synapse to SharePoint Excel automation using Power BI and Power Automate.
Evaluating the performance of Large Language Models (LLMs) is an important and necessary step in refining it. LLMs are used in solving many different problems ranging from text classification and information extraction. Choosing the correct metrics to measure the performance of an LLM can greatly increase the effectiveness of the model. In this blog, we will explore one such crucial metric the F1 score.
As the world becomes more interconnected and data-driven, the demand for real-time applications has never been higher. Artificial intelligence (AI) and natural language processing (NLP) technologies are evolving rapidly to manage live data streams. They power everything from chatbots and predictive analytics to dynamic content creation and personalized recommendations.
Retrieval-augmented generation (RAG) has already reshaped how large language models (LLMs) interact with knowledge. But now, we’re witnessing a new evolution: the rise of RAG agents —autonomous systems that don’t just retrieve information, but plan, reason, and act. In this guide, we’ll walk through what a rag agent actually is, how it differs from standard RAG setups, and why this new paradigm is redefining intelligent problem-solving.
The Llama model series has been a fascinating journey in the world of AI development. It all started with Meta’s release of the original Llama model, which aimed to democratize access to powerful language models by making them open-source. It allowed researchers and developers to dive deeper into AI without the constraints of closed systems. Fast forward to today, and we have seen significant advancements with the introduction of Llama 3, Llama 3.1, and the latest, Llama 3.2.
If you’ve been following developments in open-source LLMs, you’ve probably heard the name Kimi K2 pop up a lot lately. Released by Moonshot AI , this new model is making a strong case as one of the most capable open-source LLMs ever released. From coding and multi-step reasoning to tool use and agentic workflows, Kimi K2 delivers a level of performance and flexibility that puts it in serious competition with proprietary giants like GPT-4.1 and Claude Opus 4.
Large language models are expected to grow at a CAGR (Compound Annual Growth Rate) of 33.2% by 2030. It is anticipated that by 2025, 30% of new job postings in technology fields will require proficiency in LLM-related skills. As the influence of LLMs continues to grow, it’s crucial for professionals to upskill and stay ahead in their fields. But how can you quickly gain expertise in LLMs while juggling a full-time job?
Context engineering is quickly becoming the new foundation of modern AI system design, marking a shift away from the narrow focus on prompt engineering. While prompt engineering captured early attention by helping users coax better outputs from large language models (LLMs), it is no longer sufficient for building robust, scalable, and intelligent applications.
In the realm of data analysis, understanding data distributions is crucial. It is also important to understand the discrete vs continuous data distribution debate to make informed decisions. Whether analyzing customer behavior, tracking weather, or conducting research, understanding your data type and distribution leads to better analysis, accurate predictions, and smarter strategies.
How do LLMs work? It’s a question that sits at the heart of modern AI innovation. From writing assistants and chatbots to code generators and search engines, large language models (LLMs) are transforming the way machines interact with human language. Every time you type a prompt into ChatGPT or any other LLM-based tool, you’re initiating a complex pipeline of mathematical and neural processes that unfold within milliseconds.
Agentic AI communication protocols are at the forefront of redefining intelligent automation. Unlike traditional AI, which often operates in isolation, agentic AI systems consist of multiple autonomous agents that interact, collaborate, and adapt to complex environments. These agents, whether orchestrating supply chains, powering smart homes, or automating enterprise workflows, must communicate seamlessly to achieve shared goals.
Ever wonder what happens to your data after you chat with an AI like ChatGPT ? Do you wonder who else can see this data? Where does it go? Can it be traced back to you? These concerns arent just hypothetical. In the digital age, data is powe r. But with great power comes great responsibility, especially when it comes to protecting peoples personal information.
Model Context Protocol (MCP) is rapidly emerging as the foundational layer for intelligent, tool-using AI systems, especially as organizations shift from prompt engineering to context engineering. Developed by Anthropic and now adopted by major players like OpenAI and Microsoft , MCP provides a standardized, secure way for large language models (LLMs) and agentic systems to interface with external APIs, databases, applications, and tools.
Data normalizationsounds technical, right? But at its core, it simply means making data normal or well-structured. Now, that might sound a bit vague, so lets clear things up. But before diving into the details, lets take a quick step back and understand why normalization even became a thing in the first place. Think about itdata is everywhere. It powers business decisions, drives AI models, and keeps databases running efficiently.
In the fast-paced world of artificial intelligence, the soaring costs of developing and deploying large language models (LLMs) have become a significant hurdle for researchers, startups, and independent developers. As tech giants like OpenAI, Google, and Microsoft continue to dominate the field, the price tag for training state-of-the-art models keeps climbing, leaving innovation in the hands of a few deep-pocketed corporations.
RESTful APIs (Application Programming Interfaces) are an integral part of modern web services, and yet as the popularity of large language models (LLMs) increases, we have not seen enough APIs being made accessible to users at the scale that LLMs can enable. Imagine verbally telling your computer, “Get me weather data for Seattle” and have it magically retrieve the correct and latest information from a trusted API.
Vibe coding is revolutionizing the way we approach software development. At its core, vibe coding means expressing your intent in natural language and letting AI coding assistants translate that intent into working code. Instead of sweating the syntax, you describe the “ vibe ” of what you want—be it a data pipeline, a web app, or an analytics automation script—and frameworks like Replit, GitHub Copilot, Gemini Code Assist, and others do the heavy lifting.
Artificial intelligence is evolving fast, and Grok 4 , developed by xAI (Elon Musk’s AI company), is one of the most ambitious steps forward. Designed to compete with giants like OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude, Grok 4 brings a unique flavor to the large language model (LLM) space: deep reasoning , multimodal understanding , and real-time integration with live data.
Artificial intelligence is evolving rapidly, reshaping industries from healthcare to finance, and even creative arts. If you want to stay ahead of the curve, networking with top AI minds, exploring cutting-edge innovations, and attending AI conferences is a must. According to Statista, the AI industry is expected to grow at an annual rate of 27.67% , reaching a market size of US$826.70bn by 2030.
Large Language Models ( LLMs ) have emerged as a cornerstone technology in the rapidly evolving landscape of artificial intelligence. These models are trained using vast datasets and powered by sophisticated algorithms. It enables them to understand and generate human language,transforming industries from customer service to content creation. A critical component in the success of LLMs is data annotation, a process that ensures the data fed into these models is accurate, relevant, and meaningful
Imagine relying on an LLM-powered chatbot for important information, only to find out later that it gave you a misleading answer. This is exactly what happened with Air Canada when a grieving passenger used its chatbot to inquire about bereavement fares. The chatbot provided inaccurate information, leading to a small claims court case and a fine for the airline.
It is easy to forget how much our devices do for us until your smart assistant dims the lights, adjusts the thermostat, and reminds you to drink water, all on its own. That seamless experience is not just about convenience, but a glimpse into the growing world of agentic AI. Whether it is a self-driving car navigating rush hour or a warehouse robot dodging obstacles while organizing inventory, agentic AI is quietly revolutionizing how things get done.
Did science fiction just quietly become our everyday tech reality? Because just a few years ago, the idea of machines that think, plan, and act like humans felt like something straight from the pages of Asimov or a scene from Westworld. This used to be futuristic fiction! However, with AI agents , this advanced machine intelligence is slowly turning into a reality.These AI agents use memory, make decisions, switch roles, and even collaborate with other agents to get things done.
The world of AI never stands still, and 2025 is proving to be a groundbreaking year. The first big moment came with the launch of DeepSeek -V3, a highly advanced large language model (LLM) that made waves with its cutting-edge advancements in training optimization, achieving remarkable performance at a fraction of the cost of its competitors. Now, the next major milestone of the AI world is here – Open AI’s GPT 4.5.
While today’s world is increasingly driven by artificial intelligence (AI) and large language models (LLMs), understanding the magic behind them is crucial for your success. To get you started, Data Science Dojo and Weaviate have teamed up to bring you an exciting webinar series: Master Vector Embeddings with Weaviate. We have carefully curated the series to empower AI enthusiasts, data scientists, and industry professionals with a deep understanding of vector embeddings.
When building business apps in Power Apps with SharePoint lists as your backend, moving from a development to a production environment involves more than just copying a file. Ensuring your app continues to work seamlessly across environments, especially when using different SharePoint Lists, requires the right migration strategy. This blog provides a detailed step-by-step guide on how to migrate Power Apps from a development environment to a production environment.
In the realm of data science, understanding probability distributions is crucial. They provide a mathematical framework for modeling and analyzing data. Understand the applications of probability in data science with this blog. 9 probability distributions in data science – Data Science Dojo Explore probability distributions in data science with practical applications This blog explores nine important data science distributions and their practical applications. 1.
In the world of data, data workflows are essential to providing the ideal insights. Similarly, in football, these workflows will help you gain a competitive edge and optimize team performance. Imagine youre the data analyst for a top football club, and after reviewing the performance from the start of the season, you spot a key challenge: the team is creating plenty of chances, but the number of goals does not reflect those opportunities.
Let’s suppose youre training a machine learning model to detect diseases from X-rays. Your dataset contains only 1,000 imagesa number too small to capture the diversity of real-world cases. Limited data often leads to underperforming models that overfit and fail to generalize well. It seems like an obstacle – until you discover data augmentation.
In the rapidly evolving world of artificial intelligence , Large Language Models (LLMs) have become a cornerstone of innovation, driving advancements in natural language processing, machine learning, and beyond. As these models continue to grow in complexity and capability, the need for a structured way to evaluate and compare their performance has become increasingly important.
In the ever-evolving world of data science , staying ahead of the curve is crucial. Attending AI conferences is one of the best ways to gain insights into the latest trends, network with industry leaders, and enhance your skills. As we look forward to 2025, several AI conferences promise to deliver cutting-edge knowledge and unparalleled networking opportunities.
Let’s suppose youre training a machine learning model to detect diseases from X-rays. Your dataset contains only 1,000 imagesa number too small to capture the diversity of real-world cases. Limited data often leads to underperforming models that overfit and fail to generalize well. It seems like an obstacle – until you discover data augmentation.
What is similar between a child learning to speak and an LLM learning the human language? They both learn from examples and available information to understand and communicate. For instance, if a child hears the word ‘apple’ while holding one, they slowly associate the word with the object. Repetition and context will refine their understanding over time, enabling them to use the word correctly.
Why evaluate large language models (LLMs)? Because these models are stochastic , responding based on probabilities, not guarantees. With new models popping up almost daily, it’s crucial to know if they truly perform better. Moreover, LLMs have numerous quirks: they hallucinate (confidently spouting falsehoods), format responses poorly, slip into the wrong tone, go “off the rails,” or get overly cautious.
Applications powered by large language models (LLMs) are revolutionizing the way businesses operate, from automating customer service to enhancing data analysis. In today’s fast-paced technological landscape, staying ahead means leveraging these powerful tools to their full potential. For instance, a global e-commerce company striving to provide exceptional customer support around the clock can implement LangChain to develop an intelligent chatbot.
AI is booming with Large Language Models (LLMs) like GPT-4, which generate impressively human-like text. Yet, they have a big problem: hallucinations. LLMs can confidently produce answers that are completely wrong or made up. This is risky when accuracy matters. But there’s a fix: knowledge graphs. They organize information into connected facts and relationships, giving LLMs a solid factual foundation.
What started as a race to dominate language models with GPT and LLaMA is now moving into a new dimension: video. OpenAI and Meta, two of the biggest names in AI, are taking their competition beyond text and images into the realm of video generation. OpenAI’s Sora AI and Meta’s Movie Gen are leading this shift, offering the power to create entire scenes with just a few words.
The demand for computer science professionals is experiencing significant growth worldwide. According to the Bureau of Labor Statistics , the outlook for information technology and computer science jobs is projected to grow by 15 percent between 2021 and 2031, a rate much faster than the average for all occupations. This surge is driven by the increasing reliance on technology in various sectors, including healthcare, finance, education, and entertainment, making computer science skills more cri
Not long ago, writing code meant hours of manual effort—every function and feature painstakingly typed out. Today, things look very different. AI code generator tools are stepping in, offering a new way to approach software development. These tools turn your ideas into functioning code, often with just a few prompts. Whether you’re new to coding or a seasoned pro, AI is changing the game, making development faster, smarter, and more accessible.
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