How XENA Intelligence uses Airflow and LLMs for Amazon reviews

The course "AI Agent Fundamentals" from Databricks provided a solid foundation on LLMs, Prompt Engineering, and an exciting new product from Databricks — Agent Bricks (currently in Beta). 💡 Agent Bricks is a low-code/no-code tool designed to help users easily build and deploy AI Agents within the Databricks ecosystem. It’s fascinating to see how Databricks is bridging the gap between data and AI with such intuitive tools. Excited to explore more in this space! 🚀 #Databricks #AIAgents #LLMs #PromptEngineering #AgentBricks #LearningJourney

Processing billions of geospatial data points is no small task. In “The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI,” Alex Iannicelli at Overture Maps Foundation and Daniel Smith at Wherobots share how Airflow orchestrates massive pipelines, ensuring scale, speed and efficiency in geospatial workflows. Click the link in the comments for the episode. #AI #Automation #Airflow #MachineLearning

From Chaos to Clarity~ Here's How I Scaled Legacy ML Pipelines with Databricks + MLflow A while ago, when I was working on some ML pipelines that had grown messy over time like scattered scripts, manual versioning, and no consistent tracking. So, every time a model performed better, we struggled to answer one simple question:  “Which version produced these results?” That’s when I decided to modernize the workflow. I migrated everything to Databricks, bringing data engineering and model training into one unified environment. Then I integrated MLflow to handle experiment tracking, model registry, and reproducible runs. Here’s what it changed: - Unified data prep + model training on Databricks - Logged all experiments and metrics in MLflow for transparency - Automated model versioning and deployment through CI/CD The result? - Faster iteration cycles - Full reproducibility - Scalable pipelines ready for production Looking back, what once felt chaotic now runs like clockwork. Databricks & MLflow truly transformed how I manage and scale machine learning. #DataScience #MLOps #Databricks #MLflow #AI #MachineLearning #CloudComputing

AI is changing the rules—and databases need to keep up! Building applications that use AI effectively isn’t just about models. It’s also about handling data—structured, unstructured, and high-dimensional vector embeddings. This MongoDB blog explores key architectural considerations for tech leaders and architects managing the growing complexity of AI data. 👇 https://lnkd.in/dJMY7EMJ

AI is changing the rules—and databases need to keep up! Building applications that use AI effectively isn’t just about models. It’s also about handling data—structured, unstructured, and high-dimensional vector embeddings. This MongoDB blog explores key architectural considerations for tech leaders and architects managing the growing complexity of AI data. 👇 https://lnkd.in/d4Mag2Qa

Confirms a point I raised a year ago: SaaS will naturally extend into AIaaS and MLaaS, where experts can focus more on value creation. This inevitably triggers a debate about competitive advantage if everyone follows the same route - but the first “competitor” organizations must address isn’t external. It’s their own internal inertia, silos, and resistance to adopting this rapidly evolving tech. I see the early wave of AI agent projects acting as “Intelligent Auditors” (sic) - scanning across what already exists, surfacing gaps, and identifying hidden value. That low-friction entry point helps organizations build trust in the technology while uncovering quick wins, before stepping into more advanced, transformative use cases. #AIaaS #MLaaS #Snowflake #DataScience #MLOps #AIagents #DataEngineer

Snowflake is bringing agentic AI to the world of predictive ML. We're excited to announce the private preview of Data Science Agent to accelerate productivity for ML teams! Snowflake’s Data Science Agent uses natural language prompts to iterate, adjust and generate a fully executable ML pipeline. Here’s what you’ll love about it: ❄️ Automate the full ML pipeline – data processing, feature engineering, training, evaluation, and deployment ❄️ Deliver fast, high quality workflows with multi-agent orchestration ❄️ Execute code in a secure sandbox to inform subsequent steps and deliver verified solutions Learn more about Data Science Agent: https://lnkd.in/g2KUwwWw Reach out to your account team to learn more and check out our our full set of capabilities for end-to-end ML at https://lnkd.in/gSZzsEei

The Day the Dashboard Lied! When AI fails, it’s rarely the model’s fault, it’s because the data stopped telling the truth. AI DataOps, through orchestration tools like Apache Airflow, gives organizations an immune system for their data. It makes accuracy a habit, not a coincidence. Because when your data loses context, your intelligence loses credibility — and credibility is the one metric you can’t afford to drift. #AIDataOps #DataTrust #DigitalTransformation #AIInfrastructure #AdaptiveAnalytix #ThoughtLeadership #Apacheairflow #fullstackdevelopment

AI is changing the rules—and databases need to keep up! Building applications that use AI effectively isn’t just about models. It’s also about handling data—structured, unstructured, and high-dimensional vector embeddings. This MongoDB blog explores key architectural considerations for tech leaders and architects managing the growing complexity of AI data. 👇 https://lnkd.in/d5adrSbP

Solving Customer Support with AI – Meet "The Semantic Detective" What if AI could solve customer support tickets in seconds instead of hours? I built an AI system that does exactly that and the results have been game-changing! The Problem - Customer support teams spend hours searching past tickets. - Many tickets remain unresolved or undocumented. - Manual lookup is slow and inconsistent. The Solution – The Semantic Detective 🔍 Instantly finds similar tickets with semantic search. 🤖 Generates AI-powered responses using RAG. 📊 Processes thousands of tickets automatically. ✨ Creates resolutions for previously unresolved cases. The Real Impact ✅ Processed 5,588+ support tickets ✅ Resolved 2,819 previously open cases ✅ Achieved high accuracy with semantic similarity matching ✅ Showcased how data quality directly impacts AI results Technical Innovation: Built using Google Cloud BigQuery Vector Search + Gemini AI, this project demonstrates how advanced AI can deliver both technical excellence and business impact. This hackathon project was a crash course in AI engineering, system design, and the importance of well-structured data. 💡 Curious to dive deeper? Explore the full project here: 👉 GitHub – https://lnkd.in/grPbwher #CustomerSupport #AI #Innovation #ProblemSolving #BigQuery #GoogleCloud #VertexAI #GeminiAI #TechForGood