Databricks Acquires Quotient AI for Reliable AI Performance

The next generation of AI agents won't just write software, but operate the data systems that modern businesses depend on. Genie Code is built for exactly that: an autonomous agent that understands your data, maintains pipelines, diagnoses failures, and keeps things running. Databricks co-founder and CEO Ali Ghodsi on what makes it different: "It's not about just generating random code snippets, but understanding the entire structure of the data problem and working through the modeling workflow the same way a data scientist or engineer would." Victor Dey covers the full story for Fast Company: https://lnkd.in/e4JKNZTC

Evolution of AI agents? - from just vibe coding towards understanding data structures, pipeline management, and diagnosis of issue of your data platform. is Genie the new co worker of your data platform team!

Genie Code is a quantum leap in productivity for data practitioners. The premise is deceptively simple: a sidebar in the Databricks UI where you can describe what you want to achieve. We’ve all seen that before. The genius of Genie Code is that it has full knowledge of your Databricks environment. It can explore the catalog for suitable tables. It can open lineage graphs. It can review MLflow experiments. It can click through dashboards. That makes it super powered to create notebooks, models, agents, visualizations and more. And if there are things it cannot do, or if your tasks need a deep understanding of your business context, Genie Code can be easily extended using MCP servers (new capabilities) and skills (specific instructions loaded on demand). I gave it a try on the day it was released, to create a notebook to produce a complex diagram of revenue forecasts. It took a couple of minutes to write the Python code which I could then review and tweak to my heart’s desire. I would have used at least an hour to do it by hand.

The evolution of the Databricks Assistant. The agentic flow is fantastic. The skills the agent has help it solve problems that my other agents cannot. That includes Claude Code, GitHub Copilot, and the cast of other characters...all of which had access to Databricks skills (md files from AI DevKit). The only downside I see is how you get it to work with VSCode and Databricks Asset Bundles.

Seeing customers leverage Databricks native AI to "clean up data, build ETL pipelines, ml models and dashboards - all within the ecosystem of Databricks" without any external tooling really highlights the emphasis on making powerful tools more accessible to more users. #databricks #dataengineering

🚨 Databricks has introduced Genie Code, an autonomous #AI agent aimed at reshaping how data work is executed. The system is designed to handle complex operational tasks across the data stack, including building pipelines, debugging failures, delivering dashboards, and maintaining production systems. 🤔 The launch reflects a broader shift toward AI agents embedded directly within data platforms, potentially reducing manual engineering overhead and accelerating development cycles across data and analytics teams…

I’ve been testing ClawData with Databricks. What stood out wasn’t just automation, it was the ability to apply real data engineering thinking through a large language model. In two chats it worked through JSON ingestion into Databricks, resolved errors, structured the data across Bronze, Silver and Gold layers, and then completed customer analysis with visual outputs. Open source tools like ClawData are lowering the barrier to complex data engineering. Business users can move faster, but that shifts the role of data experts. The focus becomes designing guardrails, enforcing modelling standards, and ensuring ethical, governed AI use, not just writing pipelines. That’s a meaningful shift, that is evolving every week for our industry. #DataEngineering #Databricks #AI #OpenSource #Analytics

🚀 Feature Engineering Best Practices for Production ML Models 🚀 Feature engineering is where domain expertise meets data science magic. But moving from prototype to *production-ready* models? That’s where best practices matter most: ✅ **Automate Feature Pipelines:** Use frameworks like Feature Store, TFX, or DBT to ensure feature transformations are reproducible and versioned. ✅ **Handle Data Drift:** Continuously monitor feature distributions for drift between training & real-world data. Early alerts prevent silent model decay. ✅ **Keep Features Explainable:** Favor features that are intuitive and auditable. This builds stakeholder trust and simplifies debugging. ✅ **Ensure Real-time Feasibility:** Production features must be available at prediction time—avoid data leakage and “lookahead” bias. ✅ **Track Data Lineage:** Log feature sources, transformation code, and schema changes for compliance and reproducibility. ✅ **Test Everything:** Implement automated tests for feature value ranges, missing values, and edge cases in both training and serving pipelines. Feature engineering isn’t just about extracting signal—it’s about robust, scalable systems that adapt to reality. What’s your biggest challenge in production ML? #MachineLearning #FeatureEngineering #MLOps #DataScience #AI #DataScience #DataEngineering #BigData

Two Databricks certifications. One mission. Combining Data Engineer Associate with Generative AI Engineer gives me the full stack - from lakehouse architecture to production RAG systems. But here's what the certifications don't teach you: The gap between "we can build this" and "we should deploy this" is where most enterprise AI initiatives stall. In regulated industries like insurance and financial services, technical feasibility isn't the bottleneck. Risk tolerance is. I'm focused on solving that gap: designing AI solutions where governance isn't bolted on after the fact - it's architected from day one. Unity Catalog for data lineage. Model cards for transparency. Audit trails for regulatory compliance. If you're building AI in a regulated industry, what's your biggest architectural challenge right now? #AI #DataEngineering #Databricks #AIGovernance #Insurance

⚠️ Why 90% of AI Projects Fail Companies are rushing to build AI systems. Most will fail. Not because of models. Because of data architecture. Common problems I see: ❌ Data scattered across multiple systems ❌ No data governance ❌ Poor data quality ❌ No feature engineering strategy ❌ No ML deployment pipeline Real AI architecture should look like this: Data Sources ↓ Ingestion Pipelines ↓ Delta Lake Storage ↓ Feature Engineering ↓ Model Training ↓ Vector Search ↓ LLM / ML Applications This is why the Lakehouse architecture matters. Without it: AI becomes a demo project instead of a production system. Fix the data platform first. Then build AI. #AI #Databricks #DataEngineering #GenAI #MachineLearning