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let do this To🚀 Unlocking the Power of Feature Engineering in Data Science Ever wondered why some machine learning models perform better than others, even when they use the same algorithm? 💡 The secret often lies in Feature Engineering — the art and science of turning raw data into meaningful insights that your model can truly learn from. --- 🔍 What is Feature Engineering? Feature Engineering is the process of creating, transforming, and selecting features (input variables) that make machine learning models smarter and more accurate. It’s not just about data cleaning — it’s about adding intelligence to your data. Think of it as preparing ingredients before cooking a perfect dish — the better the prep, the better the outcome! --- ⚙️ How It Works: 1. Understand the Data – Explore relationships, correlations, and patterns. 2. Create New Features – Derive insights like Age = 2025 - Birth_Year or Price per sqft = Price / Area. 3. Transform Data – Normalize, encode, or bin values for better model learning. 4. Select the Best Features – Use feature importance, correlation, or PCA to reduce noise. 5. Handle Missing Values & Outliers – Clean data leads to more stable models. --- 🧠 Why It Matters ✅ Boosts model accuracy and interpretability ✅ Reduces overfitting ✅ Simplifies computation ✅ Bridges the gap between raw data and real-world business insights --- 🧪 Example In a house price prediction model: From Built_Year, create House_Age = 2025 - Built_Year From Area and Price, create Price_per_sqft = Price / Area Encode Location into numerical form These engineered features help the model see what we, as humans, intuitively understand. --- 🌟 Final Thought Algorithms are powerful, but the real magic happens when you engineer the right features. In the end, it’s not just about feeding data to the model — it’s about feeding it the right data in the right form. #DataScience #MachineLearning #FeatureEngineering #AI #ML #Analytics #BigData #ArtificialIntelligence #DataPreparation #MLModels

🎥 Enterprise AI Crossroads — choosing the right GenAI path in 2025 Most teams aren’t failing on models. They’re failing on fit. In this video, I walk through a 4-question decision tree that helps enterprise leaders pick the right implementation—fast. TL;DR: Ask these four questions in order Q1 — Data Currency & Citability Do we need up-to-date, citeable facts from internal sources? → Yes: Start with RAG (hybrid retrieval + reranker). Layer Graph-RAG for multi-hop questions. → No: Go to Q2. Q2 — Behavior, Format & Scale Do we require strict behavior (tool use, JSON schema) or a smaller/cheaper model at scale? → Yes: Fine-tune (PEFT + DPO). Optionally add light RAG later for grounding. → No: Strong prompting or RAG-lite may be enough. Q3 — Content Volatility Are sources changing weekly/daily? → High: Prioritize RAG. Re-embed only “hot” data on a schedule. → Low/Stable: RAG or Fine-tune can work—run a quick break-even on tokens, infra, and data labeling. Q4 — Query Complexity Do users ask multi-hop, relationship-heavy questions (people ↔ orgs ↔ assets)? → Yes: Add Graph-RAG (knowledge graph + retrieval) for precision and traceability. Practical playbook • Start lean: RAG first for changing data; Fine-tune when format/latency/scale demand it. • Instrument evals: accuracy, latency, cost per answer, and “% answers w/ citations.” • Design for ops: pipelines for chunking, re-embeds, guardrails, & model refresh. • Don’t guess—measure the break-even between data program cost vs. serving cost. If you’re at the crossroads, this framework will save months of trial-and-error. 🔗 Watch the full breakdown in the video and tell me: where does your use case land—RAG, Fine-tune, or Graph-RAG? #EnterpriseAI #GenAI #RAG #FineTuning #GraphRAG #AIProductStrategy #MLOps #DataStrategy #LLMEngineering

Everyone talks about AI. Few talk about the data pipelines that make AI possible Even the most cutting-edge AI models still depend on a clean, reliable ETL process. We chase “generative AI,” but the real foundation is still Extract → Transform → Load The pipelines that feed those models clean, contextual, and complete data. From this book, I learned: ETL isn’t dead - it’s evolved Medallion Architecture (Bronze → Silver → Gold) isn’t just a buzzword Reverse ETL matters The takeaway? AI = good ETL + governance + iteration. I’d love to hear from other data engineers, Drop your favorite tool 👇🏻 #DataEngineering #ETL #AI #Databricks #Lakehouse

Finally, a data format designed specifically for LLMs that actually saves money. TOON (Token-Oriented Object Notation) is emerging as a clever solution to a problem many developers face: the hidden costs of feeding structured data to LLMs. The format tackles the token tax problem head-on by eliminating repetitive JSON keys in uniform datasets. Instead of repeating field names for every record, TOON declares the schema once and uses CSV-style rows. The results are compelling - benchmarks show 30-60% token savings on tabular data, with some datasets seeing reductions from over 15,000 tokens down to under 9,000. What makes this particularly interesting is that LLMs actually perform better with TOON format, achieving 70.1% accuracy versus 65.4% with JSON. The format borrows smart ideas from existing standards: minimal quoting like YAML, indentation over brackets, and explicit array lengths that help LLMs validate structure. While TOON is not meant to replace JSON everywhere, it fills a specific niche for AI applications processing large uniform datasets where token costs matter. Check out the article here: https://lnkd.in/enjq2hfq If you like this content, consider subscribing to my weekly newsletter where I share 3 key events in the data and AI space, 2 BigQuery tips, 1 thing that piqued my curiosity - completely FREE. Follow the link below to sign up.☟ www.beardeddata.com/signup #DataFormats #LLM #TokenOptimization #JSON

Yesterday, I announced my mission: master Data Strategy using NotebookLM as my core tool, guided by my AI mentor, Athena 🦉. But funny enough, Athena didn’t exist until I came up with this challenge! So welcome to Day 1 of the NotebookLM masterclass, where I teach you how to create the brain that powers ANY chatbot! Why start here? 🤔 Generic prompts = generic AI results. To build truly strategic AI partners, you need world-class system prompts. So today, I built my AI Prompt Engineering Lab in NotebookLM. My 3-Step Process: ⚙️ 1. Ingest Expert Resources: Fed NotebookLM top guides & video transcripts on prompting (using the 'Discover' feature). 2. Synthesize Best Practices: Used NotebookLM's cross-source Q&A to distill core techniques (like CO-STAR, Chain-of-Thought). 3. Critique & Iterate: Got AI feedback from the Lab itself to refine and reconstruct Athena's system prompt! (Game-changer!) This lab turns prompting from art into a repeatable science. 🔬 The Data Strategist Edge? 🤑 # Command, Don't Just Query: Engineer AI for complex, repeatable tasks. # Build Custom Tools: Create specialized AI assistants (like Data Visualization Critique!). # Foundation for Automation: Master the 'brain' for automation tools like n8n. What custom AI would YOU build first with a Prompt Engineering Lab? Share below! 👇 Want the detailed workflow + the actual V2 prompt I crafted for Athena? It's in today's FREE newsletter deep dive. Link in bio! Tomorrow's Challenge: Applying this strategic thinking to learn SQL—the 80/20 way. Stay tuned! #NotebookLM #AI #PromptEngineering #SystemPrompt #LearnAI #FutureOfWork #42DayChallenge #AIApprentice #DataStrategy

ASON vs TOON: I tested both token-optimized formats on real data. Here's what happened. TOON promised 30-60% token reduction for LLM workflows. It delivered. But ASON pushes it further. 📊 Real test (28KB dataset): • JSON: 2,709 tokens (baseline) • TOON: 1,816 tokens (32.96% reduction) • ASON: 1,808 tokens (33.26% reduction) ASON wins by 8 tokens. Small difference? Not at scale. 💰 The money impact: Processing 10M API calls/month on GPT-4: • JSON: $270,900/month • TOON: $181,600/month (saves $89,300) • ASON: $180,800/month (saves $90,100) That's an extra $9,600/year saved with ASON. 🎯 Key differences: ASON automatically detects: ✓ Uniform arrays → CSV-style headers ✓ Repeated objects → Reference deduplication ✓ Duplicate values → Inline dictionary TOON requires manual configuration. 📈 Where ASON wins: • E-commerce orders: 10.24% better than JSON • Analytics time series: 23.45% better than JSON • Mixed structured data: 8% fewer tokens than TOON 📉 Where TOON wins: • Non-uniform data structures • When you need proven, stable format 🔧 Implementation? Both are dead simple: TOON: encode(data)  ASON: compressor.compress(data) My take: If you're processing millions of tokens monthly with structured, pattern-rich data, ASON's automatic optimization makes the difference. 🔗 Try it: npm install @ason-format/ason 📖 Docs: https://lnkd.in/dUFE8-pA Are you tracking your LLM token costs? What's your biggest bottleneck? ♻️ Useful? Share with someone optimizing AI costs. ➕ Follow for more on AI optimization. #AI #MachineLearning #LLM #TokenOptimization #CostOptimization #ASON #TOON #DataFormats

🔥 One SQL editor. Many wins. Zero friction. Just wrapped “SQL Analytics on Databricks” — and honestly, this feels like the future of analytics. Governance ✅ Performance ✅ AI ✅ Speed ✅ Highlights that instantly leveled up my workflow: ✨ Parameters — preset date ranges, editable at runtime, no more hard-coded filters. 🤖 AI SQL Assist — natural-language to query, instant fixes, ask follow-ups like ChatGPT. 📊 Instant Visuals — charts appear right below results — insights without switching tools. 🕒 Inline Query History — compare past runs & performance inside the editor itself. ⚡ Faster Flow — command palette, tab switching, versioned outputs — speed maxed. Outcome: cleaner governance, faster insights, and shareable SQL that scales across teams. If AI + analytics + user speed is the future — Databricks is already living in it. 🚀 #Databricks #SQL #UnityCatalog #AI #Analytics #BusinessIntelligence #DataEngineering

A great use case for data analysts and business analysts While dogfooding Rhombus AI (using our own product internally), I was honestly blown away by how powerful the platform has become. Watch the video to see why! Important: Rhombus AI covers your entire data journey from ingestion to analytics, and it can produce a wide range of visualizations - including choropleth maps, sunburst charts, spider maps, waterfall charts, funnel charts, Sankey diagrams, treemaps, network graphs, Gantt charts, heatmaps, and more - all from a single prompt. More use cases coming soon for data scientists and data engineers as well, in addition to data analysis. P.S. Imagine how much time it will save data analysts. It probably would have taken a day to produce this, but using Rhombus AI, it took me less than 2 minutes.

Just left a great session by Slawomir Tulski and prompt BI on the future of data engineering. A lot of it hit home for my own software engineering work. The biggest takeaway was that AI doesn't replace the hard part: the thinking. You still have to understand the 'why' the business, the data, the real problem, before you can even build a solution. He also made a great point about not skipping the fundamentals. It's tempting to lean on AI, but if you don't know the basics, you're stuck when the tool can't help. Big thanks to Sławomir and prompt BI. #DataEngineering #AI #SoftwareEngineering #PromptBI #CriticalThinking

𝐇𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚𝐧 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐃𝐨𝐦𝐚𝐢𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝗣𝗮𝗿𝘁 𝟮: 𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗧𝗵𝗮𝘁 𝗪𝗼𝗿𝗸𝘀 In Part 1, we discussed why traditional RAG fails at complex queries. Here’s the hybrid architecture we built to solve it: 𝗖𝗼𝗿𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: 𝗘𝗻𝘁𝗶𝘁𝘆 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵 Companies → Financial Metrics → Customer Networks → Market Segments 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Hierarchical clustering identifies thematic patterns across datasets 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 Vector Search → Graph Traversal → LLM Synthesis 𝗟𝗲𝘀𝘀𝗼𝗻 𝟭: Your Schema Determines Everything Don't model data points—model relationships. Validate against real queries before scaling. A bad schema will haunt you forever. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟮: Multi-Hop Reasoning Is Worth The Complexity If your queries need 4-5+ hops, GraphRAG wins. If not? Keep it simple. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟯: Data Quality Breaks Or Makes Your System In traditional RAG, messy data is annoying. In graphs? It's catastrophic. Entity resolution and deduplication aren't optional. Naming inconsistencies ripple through your entire system. Three more lessons changed everything about how we deploy and maintain this in next part. 𝐒𝐭𝐚𝐲 𝐓𝐮𝐧𝐞𝐝!! ← 𝗣𝗮𝗿𝘁 𝟭 | 𝗣𝗮𝗿𝘁 𝟯 → Ready to build more reliable and production-ready AI systems? Let's connect and explore how we can help. 𝑳𝒆𝒂𝒓𝒏 𝒎𝒐𝒓𝒆: www.secondbrains.in #Secondbrain #GraphRAG #EnterpriseAI #KnowledgeGraphs #AIArchitecture #AIAgents