Naman Bansal’s Post

Have you heard about Synthetic dataset before? Synthetic dataset are artificially generated data. Which can be used by different practitioners, such as AI engineers, ML engineers, and data analysts. It is a collection of data, that is not collected from real life events or observations. Algorithm and Models are behind the scene to mimic statistical properties of real data. You can use synthetic datasets to train ‘test machine learning models’, test algorithms, and simulate different scenarios without waiting for real life data. Data analyst can rely on this to learn different analysis tool because they have access to generate different datasets as they will or as they think Companies or organizations can give you a synthetic dataset that mimics their operations if they want to protect some of their data. If a dataset is scarce for a particular real-world use case, you can easily depend on the synthetic data set, which is not expensive to generate as compared to the expense of getting real-world data sets. Things to note: •⁠ Ensure that the data generated represents the real world use case. •⁠ Try to validate models that are trained on synthetic data •⁠ ⁠Be mindful of how you use synthetic data in sensitive domain such as health care and law (make sure you do not use your hand to draw case for yourself) ***** Don’t ever forget that it is a synthetic data, and it may not capture the complexities of real world data. At Autogon AI, We have the generate datasets tool on our Playground, but I still like building my own interface from the API, so that developers can be able to know that, they can build most of our tools on their related project. Try it out and tell me what you feel. (You can contribute to the project responsiveness) Generate Dataset: https://lnkd.in/duBfTrmz Github Link: https://lnkd.in/dU2hAEBn #SyntheticData #AI #MachineLearning #AutogonAI

🚀 Exciting #AI advancements are revolutionizing industriest! 🌟 Our blog "Transforming Industries with AI: The Synergy of RAG, SQL Text Queries, and Large Language Models" dives into the transformative power of these technologies. Here are 3 key takeaways: 1️⃣ RAG Innovation: Retrieval-Augmented Generation (RAG) integrates real-time data into AI, boosting accuracy and reliability. It's powering #ChatGPT and other #GenAI apps, ensuring responses are informed and up-to-date. 📊 2️⃣ SQL Text Queries: SQL-based text-to-query techniques enable AI to interact with databases more naturally. This synergy creates a bridge between AI's conversational skills and complex data systems, unleashing new business insights. 🔍 3️⃣ LLM Flexibility: Large Language Models (LLMs) like #GPT4 are more versatile thanks to RAG. By adding context from external databases, they deliver more precise, domain-specific responses across various applications. 🧠 In a nutshell, RAG is not just a fancy prompt engineering tool; it's a gateway to next-level AI performance. The future is bright, and we are just scratching the surface of AI's potential to enhance decision-making and streamline operations. Stay tuned for more insights and breakthroughs! ✨ Embrace the AI revolution with #RockRiverResearch. Let's harness the power of AI to drive innovation and success. Follow us for more updates! #AIInnovation #BusinessTransformation #TechnologyTrends #ArtificialIntelligence #DataScience #MachineLearning #FutureOfWork #AIConsulting #StrategicInsights #AIforBusiness https://lnkd.in/eJpvFQvw

Etherwire: Ai can never replace Excel The world runs on spreadsheets. Long before modern machine learning tools came around, Excel was where most data lived. Fast forward to today, and not much has changed. Some people thought that with tools like ChatGPT, we wouldn't need Excel anymore. They believed that data science could be done just by letting large language models understand the data. But Excel is still going strong. It’s being said, AI can never replace Excel, seems interesting? Read more about it at: https://lnkd.in/dBPv2ng6 #ChatGPT #AiNews #AiUpdates #LatestNews #Excel #AivsHuman

Have you met your data transformation copilot yet? ✨ AI Transform lets teams use natural language to describe how they'd like their data to change. Flatfile takes this prompt and generates and executes the code necessary to make changes instantly across your entire data set. Find out how AI Transform can help you transform your data import processes: https://buff.ly/3UxB9WX #ai #artificialintelligence #dataonboarding #developers #nlp

📣 New in MEAP! 📣 Data Analysis with AI and R, by Ulrich Matter. 📚 Become a more productive—and a more valuable—R data professional! Our latest MEAP release - Data Analysis with AI and R - demonstrates how to enhance your R code and #DataAnalytics workflows using #LargeLanguageModels (#LLMs) and #GenAI. The book introduces potent #AI techniques that can enhance your speed and precision in writing #Rstats programs and aid in the creation of effective #DataAnalysis pipelines. 🚀 Take action now! Save 50% with code limatter50 at https://mng.bz/6YOD 📖 Get into the book: https://mng.bz/QVYw 📹 Check out this video to find out: https://mng.bz/4pqa #ManningBooks #LearnwithManning

Methods for generating synthetic descriptive data https://lnkd.in/dmjEwvc3 AI News, AI, AI tools, Innovation, itinai.com, LLM, Matt Collins, t.me/itinai, Towards Data Science - Medium **Generating Synthetic Descriptive Data in PySpark** Are you looking for practical ways to generate synthetic descriptive data in PySpark? Our latest article covers various methods to quickly create text data for artificial datasets. Synthetic datasets are valuable for demonstrating data products, exploring machine learning algorithms, and testing data engineering pipelines. **Key Methods for Generating Synthetic Data:** 1. **Random Characters:** Start with simple built-in functionality to generate random text data. While this method has limited applications in demo datasets, it can be combined with other techniques for added value. 2. **APIs:** Utilize APIs to obtain representative data for various topics such as currency rates. While getting data from APIs can vary in complexity, third-party packages like Faker can provide high-impact and low-cost solutions. 3. **Large Language Models (LLMs) like ChatGPT:** These models excel in rapid data generation, but their reliability and consistency are under investigation. **Implementing Synthetic Data into a DataFrame:** Explore options such as User-Defined Functions (UDFs) and Databricks Labs Data Generator to implement synthetic data into a DataFrame. **Conclusion:** Our article outlines practical methods to generate textual synthetic data quickly, accelerating demonstrative dataset creation. These examples can be extended, refined, and tailored to your specific use-case. **Unlock the Power of AI for Your Company:** Discover how AI can redefine your way of work and identify automation opportunities. Connect with us at hello@itinai.com for AI KPI management advice and continuous insights into leveraging AI. **Spotlight on a Practical AI Solution:** Explore the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. **Useful Links:** - AI Lab in Telegram @aiscrumbot – free consultation - [Methods for generating synthetic descriptive data](https://lnkd.in/gG2gaQJQ) - [Towards Data Science – Medium](https://lnkd.in/dvzs9kXm) - Twitter – @itinaicom *Stay tuned for more insights into AI solutions and practical applications!*

AI < ETL If you know what AI stands for, but not ETL, please keep reading 🗣️. In the world of fancy Data acronyms, we’ve somehow drifted way from the most important acronym to the “shiniest” one. Think AI will read all your data and give you the answers you’re looking for? Try answering these questions first.... Do you know how big your dataset is? 🤔 Do you know how much it will cost to run this “AI” on it? 💰 Did you remove outliers or anomalies? 😵💫 Are you able to fact check what it produces? ✅ If not, AI can’t fix what you ignore. #AI #ChatGPT #ETL #DataScience

Use cases for #generativeAI and other #artificialintelligence technologies have captured the collective imagination of #businessleaders and #decisionmakers across every industry. Alan Jacobson, chief data and analytics officer at Alteryx, explores the #techskills required for future success as an #AI #promptengineer — also referred to as an '#AIwhisperer'. https://lnkd.in/eM4BrNQJ

With the transformative capabilities available with GenAI and Large Language Models (LLM's), it is more important now than ever before to prepare your data to be used with AI. Is your Data Strategy and Data Governance ready for GenAI and LLM's? #GenAI #LLM #ML #AI #datastrategy #datagovernance #predictiveanalytics https://lnkd.in/e2xFunbR

Introducing PyGraft: AI Tool Creating Versatile Schemas and Knowledge Graphs #PyGraft 🤝 Follow us on Discord 🔜: https://lnkd.in/gt823Zd3 _ ❇️ Summary: The article discusses the usage of knowledge graphs (KGs) as a popular method for representing data in a graph structure. However, relying on mainstream KGs for evaluating newly proposed models may not be sufficient, as they often have shared statistical properties and biases. Synthetic KGs are needed to create fictitious yet realistic datasets for testing novel models in various data contexts. The researchers from Université de Lorraine and Université Côte d’Azur have introduced PyGraft, a Python-based tool for creating highly customized, domain-neutral schemas and KGs. The tool is adjustable based on user-specified criteria and adheres to common Semantic Web standards. The code is publicly released for ease of use. Hashtags: #chatGPT #PyGraftAI #CustomizedSchemas