Analytics Vidhya

Leading with Data Ep 52 - The Journey of Innovation: 30+ Patents, 2 AI Startups: https://lnkd.in/g5rZ-UFm This week, Kunal Jain sat down with Anand Ranganathan, Chief AI Officer and Co-founder of OneByZero. A seasoned technology leader with over a decade of experience at IBM and multiple startups under his belt, Anand now leads global consulting projects in Generative AI, Machine Learning, and Data Platforms, while building a talented team of over 50 professionals across three countries. Here’s a sneak peek of what you’ll learn in this insightful episode: - Anand’s journey into data science and AI, and what his early days in the field looked like. - Why he left a decade-long career at IBM to venture into entrepreneurship and the challenges he tackled. - The story behind his first startup, Unscramble, and its milestones. - How he adapted to the structural differences between building Unscramble and OneByZero. - Key lessons learned from his journey and what he’d do differently now. - Exciting future trends in AI and the evolution of agentic workflows. - His advice for aspiring professionals, drawing from his 30+ patents, extensive industry experience, and entrepreneurial journey. Don't miss out on this thought-provoking conversation! #AnalyticsVidhya #LeadingWithData #LLMs

To truly become an expert in GenAI Ops, the key is not just knowing what to learn, but how to learn it and apply it effectively. The journey begins with gaining a broad understanding of foundational concepts such as prompt engineering, Retrieval-Augmented Generation (RAG), and AI agents. However, your focus should gradually shift to mastering the intersection of Large Language Models (LLMs) and AI agents with operational frameworks – LLMOps and AgentOps. These fields will enable you to build, deploy, and maintain intelligent systems at scale. Here’s a structured, week-by-week GenAI Ops Roadmap to mastering these domains, emphasizing how you will move from learning concepts to applying them practically: https://lnkd.in/gJSQNt_U #AnalyticsVidhya #LLMs #GenAIOps #2025

In the ever-evolving landscape of natural language understanding, researchers continue to push the boundaries of what’s possible through innovative approaches. In this article, we will delve into a collection of groundbreaking research papers on generative AI (GenAI). They explore diverse facets of language models, from improving alignment with human preferences to synthesizing 3D content from text descriptions. While contributing to the academic discourse, these studies also offer practical insights that could shape the future of natural language processing. Let’s embark on a journey through these enlightening investigations: https://lnkd.in/gc5a7JUu #AnalyticsVidhya #DataScience #Listicle #GenAI

What if collaborating with AI could feel as natural as working with a teammate? That’s what OpenAI’s new feature, Canvas, is for! Launched on the 4th day of the 12 Days of OpenAI series, Canvas is now available for all ChatGPT users: https://lnkd.in/g9kB_iuu Whether you’re crafting a compelling story, debugging complex code, or exploring new ideas, Canvas brings a side-by-side workspace that adapts seamlessly to your needs. In this article, Nitika Sharma takes a hands-on approach with ChatGPT Canvas #AnalyticsVidhya #ChatGPT #OpenAI

AI agents are designed to act autonomously, solving problems and executing tasks in dynamic environments. A key feature in Autogen, enabling their adaptability is AutoGen’s code executors. This feature along with LLMs enables AI agents to generate, evaluate, and execute code in real-time. This capability bridges the gap between static AI models and actionable intelligence. By automating workflows, performing data analysis, and debugging complex systems, it transforms agents from mere thinkers into effective doers: https://lnkd.in/g7-j9PKA #AnalyticsVidhya #DataScience #GenAI #AIAgents

Comprehensive Roadmap to become a RAG Expert in 2025! This Roadmap is for: - Python developers & ML Engineers who want to build AI-driven applications leveraging LLMs and custom enterprise data. - Students and Learners willing to dive into RAG implementations and gain hands-on experience with practical examples. Moreover, we are offering a Free Certification Course on RAG: https://lnkd.in/gW_qSZpn #AnalyticsVidhya #RAG #Roadmaps

We tested OpenAI's SORA model on various prompts and compared it with AWS NOVA: https://lnkd.in/gyGYjyNX What do you think? Which model performed better? #AnalyticsVidhya #SORA #NOVA

2024 has been nothing short of revolutionary for the world of generative AI. With a slew of groundbreaking innovations, the generative AI landscape has evolved in ways that are reshaping industries and enhancing everyday experiences. From new open-source models and multimodal capabilities, to AI agents and beyond, the advancements of 2024 reflect a collective ambition to push the boundaries of technology. In this article, K. C. Sabreena Basheer will explore the top 20 generative AI developments that have defined 2024, and will continue to shape the future of AI: https://lnkd.in/gAwE-9Fc #AnalyticsVidhya #LLMs #RAGs #2024

The recent launch of OpenAI’s Sora and Amazon’s Nova under the Bedrock platform marks an exciting new chapter in AI. While both models advance the field in their own ways, they cater to different goals. Sora focuses on turning text into video, bringing new creative options to content makers. Nova, meanwhile, is geared toward broad business applications, making it simpler for companies to add AI tools to their daily work. In this article, Janvi Kumari will give both models (OpenAI Sora vs AWS Nova) the same prompts, compare their outputs, and highlight their strengths and weaknesses. This side-by-side look will help you understand how these two AI systems differ and where each might shine: https://lnkd.in/gKu8Nmbt #AnalyticsVidhya #AWS #Nova #OpenAI

HuggingFace Spaces is a platform that enables developers and researchers to create, deploy, and share machine learning applications effortlessly. Spaces provide a simple and collaborative environment to host interactive demos of machine learning models using frameworks like Gradio and Streamlit. It integrates seamlessly with HuggingFace’s model hub, giving access to thousands of pre-trained models across domains such as natural language processing (NLP), computer vision, and audio: https://lnkd.in/ge3B84gg #AnalyticsVidhya #HuggingFace #LLMs #RAG