Digitalization is a lot more than scanning and scrolling. Kneat’s expert, Darragh Boyle, shows you how to unlock the real benefits of digitalization, including how to use it for Risk-Based CSV. Register Now: 🔗 https://hubs.li/Q02FNm8Q0
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Don't miss this session. Walk away with a #validation transformation roadmap and best practices based on Hong's recent rollout!
Attending the KENX CSV conference? Don't miss Robin Smallwood and Hong Wa Leong share actionable strategies for improving your CSV process. https://bit.ly/3uPaRoo
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Don't miss this roadmap session on how to digitize #validation from #quality risk assessments through vendor qualification tips, rollout management, and optimization.
Attending the KENX CSV conference? Don't miss Robin Smallwood and Hong Wa Leong share actionable strategies for improving your CSV process. https://bit.ly/3uPaRoo
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🌟 Did you know you can export measurement data as a CSV file? 📊 With Ace Media, presenting reliable metric data in your own way is a breeze! Save hours of time and focus on what matters most. #AceMedia #DataExport #Efficiency #PRMeasurement
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What is CSVizer for? To find a quick answer, please watch this video.
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📢 Simplifying CSV to XLSX Conversion with this Handy Macro! If you've been spending too much time converting CSV files to XLSX, we've got your back! Introducing a time-saving solution - a custom macro to automate the process. How to Use: Organize Your CSVs: Gather all your CSV files into a single folder. Get the Macro: Copy the macro code from the provided Notepad document. Edit the Location: Inside the macro code, replace the file location with the path to your CSV folder. Run the Macro: Execute the macro to convert all CSVs to XLSX files effortlessly. Say goodbye to manual conversions and hello to increased productivity! Get the macro code and detailed instructions here - https://lnkd.in/gk8D3uSA 🚀 #TimeSaver #CSVtoXLSX #ProductivityBoost #dataanalytics #macro
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Great example that shows 1) One cap doesn't fit all. You don't need a single LLM for everything 2) With fine tuning you can get better performance from a small model than a huge LLM with trillions of hyperparameters. Result: more efficiency and lower TCO!
🚀 I'm a fan of RAG, but there is potentially untapped alpha in fine-tuning, especially in Life Sciences where proprietary data and nuanced terminology are common, the effectiveness of general LLMs can be limited. With Snowflake Cortex-finetuning, it took me less than 5 minutes to fine-tune the LLaMA-8B, creating a new model, Device_LLaMA_8B, to accurately answer questions based on proprietary sample data in a table. 🔍 The challenge? Analyze raw customer complaint texts from the devices sold by MediSnow (a fictitious company) and determine which Business Unit (BU) manufactured each device. 📉 LLaMA-8B by itself: Small, cheaper to run, but not very powerful. 💪 LLaMA-70B: More powerful, but lacks context. For instance, it has no clue which Business Unit manufactures the QuantumNova X5, a device MediSnow produces and sells. 💡 The solution: Fine-tune the smaller LLaMA-8B, making it both affordable and knowledgeable about MediSnow-specific data. 💬 Again, RAG is a good approach, but remember, you pay for tokens IN and OUT. So, in some cases, front-loading that cost by fine-tuning could be a better approach as opposed to building increasingly complex prompt pipelines with RAG. 🔧 Follow this quick tutorial step-by-step (can be done in under 15 minutes): https://lnkd.in/gcyuXJqQ
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🚀 I'm a fan of RAG, but there is potentially untapped alpha in fine-tuning, especially in Life Sciences where proprietary data and nuanced terminology are common, the effectiveness of general LLMs can be limited. With Snowflake Cortex-finetuning, it took me less than 5 minutes to fine-tune the LLaMA-8B, creating a new model, Device_LLaMA_8B, to accurately answer questions based on proprietary sample data in a table. 🔍 The challenge? Analyze raw customer complaint texts from the devices sold by MediSnow (a fictitious company) and determine which Business Unit (BU) manufactured each device. 📉 LLaMA-8B by itself: Small, cheaper to run, but not very powerful. 💪 LLaMA-70B: More powerful, but lacks context. For instance, it has no clue which Business Unit manufactures the QuantumNova X5, a device MediSnow produces and sells. 💡 The solution: Fine-tune the smaller LLaMA-8B, making it both affordable and knowledgeable about MediSnow-specific data. 💬 Again, RAG is a good approach, but remember, you pay for tokens IN and OUT. So, in some cases, front-loading that cost by fine-tuning could be a better approach as opposed to building increasingly complex prompt pipelines with RAG. 🔧 Follow this quick tutorial step-by-step (can be done in under 15 minutes): https://lnkd.in/gcyuXJqQ
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🔍 Looking for reliable metric data? Along with a digital report, Ace Media’s Reports tool allows you to export all your measurement data as a CSV file. 📁 Say goodbye to hours of manual research! #DataAnalysis #PRTools #AceMedia #PRMeasurement
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📈SuretyDoc now understands CSV's📈 We're excited to bring you some fantastic news that will make managing your data a breeze! SuretyDoc has taken a leap forward in its capabilities, and we're thrilled to announce that we now fully understand and support CSV files. #datamanagement #csv #simplify #SuretyDocUpdate #datahandling #businessgrowth #datasolutions #productivityboost #streamlinedprocesses #dataanalysis #SuretyDocAdvancements #data #EnhancedCapabilities #techinnovation #simplicity #smarter #productivityenhancement
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