Quality Modeller’s Post

What could you do with 1B URLs of Data per day? Every 15 minutes, our customers scrape enough data to train ChatGPT from scratch. https://lnkd.in/etWZ6fDT #datacollection #dataforai #datascience

Traditional Search Engine is not dead nor a data (document) scientist; but gives goose bumps. Some fast moving areas of development... seriously looks like i do not need Google search engine and do not need a data scientist. The problem is both are diffident and does not go beyond saying there could be errors and yet do a great job ! Meta, via Whatapp, using LLAMA 3, has become a search engine... (see the image in the comment section ) chatGPT via interaction, has become an doc/spreadsheet/data analyst (go to the link below which I posted as public link) Here is my quick analysis of Titanic train data which chatGPT split it as training and testing completing the analysis and providing insights. ...

The Magical ChatGPT Code Interpreter Plugin — Your Personal Programmer and Data Analyst. If you are like me, you do not know much about AI or ChatGPT yet. And now it has a Code Interpreter so what does that mean? This article helps with knowing with it is and how you can use it.

Completing the data camp course "Introduction to ChatGPT" to revise the key points using AI-powered tools as such and make a better utilisation of it.

We can't all rely on ChatGPT and YouTube to learn Data Vault. 🤔 Check out our Data Vault for Developers course which will help you build a solid foundation for working with Data Vault - https://bit.ly/4aQfb68 #DataVault #DataEngineering #Developer #DataEngineer #AI #ChatGPT

Leveled up to Level 5 in AI Journey!💻📚 At this level, I learnt about Retrieval Augmented Generation (RAG)."The previous module by Pathway x GTech MuLearn covered discussions on RAG, power up the LLM with real-time accurate data."🖋️ •All of us know about chatGPT, But we must know about its most powerful techniques to improve pre-trained Large Language Models RAG, it could scour the web, understand PDFs, and learn from our own data? That's the power of RAG! 1.RAG supercharges Large Language Models (LLMs) like ChatGPT by giving them access to: Fresh web data: No more outdated responses! Custom PDFs and text files: Train on the data that truly matters to you! RAG mitigates these challenges by providing a cost-effective solution that ensures model accuracy without the need for frequent and expensive retraining cycles, making fine-tuning effective with significant resource investment in data preparation, retraining, and deployment. 2. Prompt Engineering vs RAG 📝 Although prompt engineering may seem like a lighter alternative, it comes with its own set of challenges, including data privacy concerns, inefficient data retrieval, and technical limitations due to token constraints.at last, RAG emerges as a more viable and efficient option for addressing the challenges presented by prompt engineering and fine-tuning. RAG's Advantages: Cost-Effective: RAG models with vector embeddings API are roughly 80 times less expensive than commonly used fine-tuning APIs. Data Freshness: RAG ensures the model delivers current and pertinent output without frequent retraining. Efficient Retrieval: Vector indexing in RAG enables quick and semantically accurate data retrieval. No Token Limit Constraints: RAG's approach of storing data in efficient vector indexes facilitates dealing with large and complex data sets. 3.Challenges with Fine-Tuning: Data Preparation Challenges: Addressing biases and ensuring balanced data distribution demand in-depth data analysis skills. Cost Efficiency: Retraining and deployment are time-consuming and financially taxing. Data Freshness: Model accuracy can decline if data isn't regularly updated, requiring frequent and costly retraining. at last, RAG emerges as a more viable and efficient option for addressing the challenges presented by prompt engineering and fine-tuning. #AI #RAG #LLM #CostEfficiency #Technology #GenAIBootcamp #GTechMuLearn #pathway

In one of our internal upskilling meetings at Massive Rocket | Global Braze & Snowflake Agency we were discussing about the Data Analysis capabilities of ChatGPT. It got me wondering if it really could achieve so much? I recently got introduced to a course on ChatGPT Advanced Data Analysis (Thanks to Lorna Argeñal) and I've realized that there are just 2 ways to it: 1. Do not go gentle into that good night: Unlearn to do everything by yourself and then learn to co-exist with AI 2. Be the single leaf that stands against the wind I think it would be wise to choose the first😅

🔍 Tackling Statistical Analysis Challenges with AI Solutions 📊🧠 Statistical analysis often presents numerous challenges—from crafting a comprehensive plan based on specific research questions and objectives to managing and analyzing complex datasets, and finally, visualizing the results effectively. These hurdles can significantly slow down progress and accuracy. But what if AI could streamline this entire process? In my latest exploration, I show how ChatGPT can revolutionize data analysis. By leveraging advanced AI, we can: Develop precise statistical plans tailored to specific research needs. Efficiently upload, manage, and analyze datasheets to uncover key insights. Generate clear and insightful graphs, charts, and visualizations with ease. This AI-driven approach saves time and enhances the accuracy and clarity of our data analysis. If you're looking to overcome the common obstacles in statistical analysis and make your workflow more efficient, this is a game-changer. Discover more about these innovative solutions here: https://lnkd.in/dZARC4Ad #DataAnalysis #AI #ChatGPT #Research #DataScience #MachineLearning #Innovation #DataVisualization #Efficiency

🚀 When and Why to Fine-Tune an LLM🚀 As LLMs like Llama 70b, Mixtral, ChatGPT, and Claude continue to advance, the question arises: Is fine-tuning still necessary? Here are some scenarios where fine-tuning can significantly enhance your outcomes: 1. Prompt Engineering Limitations Start with prompt engineering for a quick MVP. However, if it falls short after several iterations, fine-tuning might break through performance barriers. Establishing a robust evaluation suite is crucial to measure response quality accurately. 2. Complex Context Needs When embedding all necessary context in a prompt is impractical—such as coding in a proprietary language—fine-tuning becomes essential. Encoding extensive specifications in prompts can be infeasible or too costly due to context limits. 3. Unique, High-Quality Data Fine-tuning shines with unique, high-quality data. If you have specific tasks and data that the base model hasn't encountered, fine-tuning can significantly boost performance by leveraging this exclusive dataset (w/ enough number of examples). 4. Improved Latency and Cost A fine-tuned, smaller model can outperform a large foundation model for specific tasks, resulting in shorter prompts, reduced costs, and faster inference times. This efficiency can be crucial for scalable applications. Fine-tuning isn't always necessary, but it can provide a competitive edge in the right scenarios. Evaluate your project needs and data to make an informed decision. #LLM #MachineLearning #FineTuning #AI

Multiple AI providers down? Don't sweat it, just have a fallback plan. 🚨 𝗪𝗵𝗮𝘁'𝘀 𝗴𝗼𝗶𝗻𝗴 𝗼𝗻? OpenAI’s ChatGPT and Anthropic’s Claude & Perplexity experienced unexpected downtime issues this morning. ChatGPT has been down for ~5 hours, at the time of writing, while Anthropic has been in and out over the last ~2 hours. The outage could signal infrastructure, scaling issues, or something else. 🤨 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿? It's true, outages happen. But for developers building critical applications, particularly those building RAG applications, this moment highlights the importance of proactive planning. Relying solely on OpenAI's Assistant API in your RAG projects 𝙘𝙧𝙚𝙖𝙩𝙚𝙨 𝙖 𝙨𝙞𝙣𝙜𝙡𝙚 𝙥𝙤𝙞𝙣𝙩 𝙤𝙛 𝙛𝙖𝙞𝙡𝙪𝙧𝙚. If OpenAI experiences an outage, your entire application could go down with it. This is why staying "model agnostic" is crucial. By designing your RAG systems to work with multiple models, you ensure user experience remains uninterrupted even during unexpected downtimes. API outages, rate limits, and even bad model outputs would no longer be a roadblock. 😁 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼? You should have a model fallback strategy in your RAG projects to act as a safety net, ensuring applications handle third-party LLM provider issues without impacting users. 𝗔̲ ̲𝗱̲𝗶̲𝘀̲𝗿̲𝘂̲𝗽̲𝘁̲𝗶̲𝗼̲𝗻̲ ̲𝘁̲𝗼̲ ̲𝗢̲𝗽̲𝗲̲𝗻̲𝗔̲𝗜̲’̲𝘀̲ ̲𝗮̲𝘀̲𝘀̲𝗶̲𝘀̲𝘁̲𝗮̲𝗻̲𝘁̲ ̲𝗔̲𝗣̲𝗜̲ ̲𝘀̲𝗵̲𝗼̲𝘂̲𝗹̲𝗱̲ ̲𝗻̲𝗼̲𝘁̲ ̲𝗰̲𝗿̲𝗮̲𝘀̲𝗵̲ ̲𝘆̲𝗼̲𝘂̲𝗿̲ ̲𝗲̲𝗻̲𝘁̲𝗶̲𝗿̲𝗲̲ ̲𝘀̲𝗲̲𝗿̲𝘃̲𝗶̲𝗰̲𝗲̲.̲ However, a fallback strategy only works if you are vendor agnostic so that you can use a model for generating responses. 🐷 trufflepig provides flexibility when it comes to the generative step of RAG, preventing over-reliance on a single model provider. ✅ 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗮𝗻𝗱 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗳𝗮𝗹𝗹𝗯𝗮𝗰𝗸 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆? - Fallbacks prevent applications from crashing or becoming unresponsive during inference API failures. - Application logic branches to other inference options or provides a naive solution for the time being, minimizing disruption. - Fallbacks can handle third-party API errors like outages, rate limits, and content policies. They can even manage more complex scenarios by switching to alternative sequences of operations if a primary sequence fails. Note that utilizing a fallback strategy requires tweaking the prompt for each model to achieve similar outputs. 👍 Follow for more AI-related content, give this post a like, and comment your thoughts below. Check out trufflepig here: https://lnkd.in/gJ5_qKh7