🔥 This is an incredible guide! 🚀 The emphasis on fine-tuning and pretraining really resonates—tailoring AI models to specific needs is a game-changer. The insights on RAG (retrieval-augmented generation) are also spot-on; they’re key for boosting the quality of AI outputs in real-world scenarios. Kudos to Databricks for setting new standards in open-source LLMs and for making GenAI accessible to more developers. 🌟 #GenerativeAI #AI #MachineLearning #DataScience #TechInnovation #AIApplications #LinkedInLearning #AIForGood
👩🎓𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈: 𝐀 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐞𝐫 𝐟𝐨𝐫 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐀𝐈 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬🚀 🔍 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: ➡️ 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬: A critical starting point for deploying high-quality GenAI applications. Learn how Databricks’ DBRX sets a new standard in open-source LLMs. ➡️ 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Discover the power of automated analysis and real-time data to elevate your AI’s response quality. ➡️ 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬: Get insights into creating custom AI models tailored to your specific needs, leveraging tools like LoRA. ➡️ 𝐏𝐫𝐞𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Understand when and how to train your own models from scratch, and how to measure their effectiveness in real-world applications. 🎯 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: ➡️Generative AI is revolutionizing business operations, but achieving production quality requires the right tools and expertise. This guide is your roadmap to mastering GenAI, from foundational models to advanced deployment strategies. 📈 𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐒𝐭𝐞𝐩𝐬: ➡️Invest in your data infrastructure to support GenAI-powered applications. ➡️Focus on retrieval-augmented generation (RAG) to enhance AI output quality. ➡️Consider pretraining your own models for more control and cost-effectiveness. 💡 𝐂𝐫𝐞𝐝𝐢𝐭𝐬: This summary is based on insights from the document “Mastering GenAI: A Guide to Production-Quality GEN AI Apps”. By Databricks #GenerativeAI #AI #MachineLearning #DataScience #TechInnovation #AIApplications #LinkedInLearning