Databricks answers founders' AI questions: compliance, costs, hallucinations, and more

🧠 Databricks Research: Building Better AI Judges Is a People Problem, Not Just a Tech One New insights from Databricks reveal that the biggest blocker to enterprise AI adoption isn’t model intelligence — it’s defining and measuring quality effectively. Enter: AI Judges — systems that evaluate the outputs of other AI systems. With their Judge Builder framework, Databricks is helping enterprises move beyond vague metrics and into domain-specific, scalable evaluation. Key takeaways: 🔁 The “Ouroboros Problem”: Using AI to judge AI creates a circular challenge. Databricks solves this by minimizing the “distance to human expert ground truth.” 👥 Lesson 1: Experts often disagree more than expected. Batched annotation and inter-rater reliability checks help align teams early. 🔍 Lesson 2: Break down broad criteria into specific judges (e.g., one for factuality, one for tone). This granularity helps pinpoint what needs fixing. 📊 Lesson 3: You don’t need thousands of examples. Just 20–30 well-chosen edge cases can train effective judges. 🚀 Real-world impact: - One customer built over a dozen judges after a single workshop. - Others became seven-figure GenAI spenders after implementing Judge Builder. - Teams now feel confident using advanced techniques like reinforcement learning — because they can finally measure improvement. 📌 What enterprises should do now: 1. Start with one regulatory requirement + one known failure mode. 2. Use SMEs to annotate 20–30 edge cases. 3. Review and evolve judges regularly as systems grow. “A judge is not just an evaluator — it’s a guardrail, a metric, and a foundation for optimization,” says Jonathan Frankle, Chief AI Scientist at Databricks. #superintelligencenews #superintelligencenewsletter #AI #GenAI #EnterpriseAI #MachineLearning #AIEvaluation #Databricks #AIJudges #MLops #ResponsibleAI #PromptEngineering

Dreamforce showed us one thing: the future of DevOps is intelligent. With Org Intelligence™, powered by Copado AI, your Salesforce org becomes more than just data — it becomes actually self-aware. It sees the connections, flags the risks, and reveals the fastest path to deliver with confidence. This isn’t DevOps as usual. It’s DevOps that thinks ahead — helping teams move from chaos to clarity, from reaction to prediction. Because when your org understands itself, innovation happens naturally. Start for free and see how intelligence changes everything Start your journey → https://ow.ly/Ly5N50XgrSP #Copado #CopadoAI #OrgIntelligence #AI #SalesforceDevOps

DataMasque's “marketplace-first” strategy, leveraging the AWS Marketplace, enabled global growth and rapid US success even before building a local sales force. Now, they’re leading the shift from generative to #agenticAI with cutting-edge in-flight and API-based data masking. Watch the full interview to learn more about a startup paving the way for responsible, scalable #AI.

Scaling AI Projects Enterprise-Wide is not easy. Many companies test AI in small ways - it works great! But when they try to use it across the whole business, things break. Data silos, tech limits, missing skills, and no clear rules make scaling hard. In our latest blog, we talk about how to fix these problems - from better MLOps and governance to building trust and showing real ROI. Read the full article here: https://lnkd.in/g7mqMSTZ #AI #MachineLearning #EnterpriseAI #DigitalTransformation #Innovify #AIML

How AI labs use Mercor to get the data companies won’t share Mercor CEO Brendan Foody has built a $10 billion empire freeing up valuable data from legacy industries, and making it available to AI labs. https://lnkd.in/g9Wy94VR

Mercor CEO Brendan Foody has built a $10 billion empire freeing up valuable data from legacy industries, and making it available to AI labs.

🔥 Databricks Week of Agents is here The next wave of enterprise AI isn’t about bigger models. It’s about trusted agents that understand your data, act with accuracy, and scale safely across your business. This week, Databricks expands Agent Bricks to unify accuracy, governance, and openness in one platform so enterprises can move from isolated pilots to production-grade AI agents with confidence. 💡 Accuracy – Evaluate every agent with MLflow, trace interactions, score outputs, and collect expert feedback. 🔐 Governance – The new MCP Catalog in Databricks Marketplace integrates with AI Gateway to centralize access, auditing, and compliance. 🌍 Openness – Build any agent with GPT-5, Claude, Gemini, or Llama and connect it to your governed data, documents, or external systems. 🚀 The path to trusted, governed, and production-ready AI agents starts here. Join Sam Altman and Ali Ghodsi on Nov 11 for The Future of AI: Build Agents that Work.

In this new thesis, my teammate Ryan Wexler breaks down why the future of AI will be defined by expert data and how startups curating, labeling, and structuring this data will become the most valuable infrastructure companies of the decade. 🧩 Read the full piece here → https://lnkd.in/eHxUdxuH

What are the major tools used in MLOps (e.g., MLflow, Kubeflow, Airflow, DVC)? MLOps has become essential in streamlining machine learning workflows, and a few key tools stand out. MLflow is popular for managing the machine learning lifecycle, while Kubeflow offers strong capabilities for running ML on Kubernetes. Airflow, on the other hand, excels at orchestrating complex workflows, ensuring that all tasks are executed in the correct sequence. Lastly, DVC is invaluable for version control and data management, which is critical in ML projects. Understanding these tools can enhance your MLOps strategy, making your processes more efficient and collaborative. What tools have you found most effective in your MLOps journey? Let’s discuss your views below. #MLOps #MachineLearning #DataScience #AI #ArtificialIntelligence #TechTools