Databricks Generative AI Engineer Certification Course

🎓 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝 𝐭𝐡𝐞 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐫𝐚. 5 courses covering Delta Lake, PySpark, MLflow, Unity Catalog, and Generative AI (RAG) architectures. Focused on learning end-to-end data pipelines, distributed machine learning operations (MLOps), and enterprise data governance. Continuing to learn and expand my stack. #DataEngineering #Databricks #MLOps #GenerativeAI #ContinuousLearning

I am thrilled to share the completion of one of the most valuable and high-quality courses I took in 2026: the LLMOps with Databricks program by Maria Vechtomova and Başak Tuğçe Eskili The course taught the un-glamorous, production-shaped parts that actually decide whether an LLM system can be trusted — medallion data pipelines, retrieval grounding, agent orchestration, evaluation, model serving, and end-to-end observability with MLflow. What made it exceptional wasn't only the technical depth. It was the dedication of the instructors, who were consistently available for support, discussions, and troubleshooting throughout the entire journey — the kind of teaching that stays with you long after the last module. The real test of a course is what you can build alone once it ends. So I built: NHTSA Defect Intelligence — a natural-language safety analyst that unifies separate U.S. vehicle-safety data sources (recalls, complaints, investigations, technical service bulletins, and autonomous-vehicle crash reports) behind a single chat interface, with every answer cited back to an authoritative source ID. Bronze to silver to gold, chunking and embeddings, a Genie space and Vector Search, a four-tool agent with Lakebase-backed memory, a three-tier evaluation harness, full tracing, and a Databricks App front end. I will share the details of the project in a separate post. It fought back at every layer — and that's exactly where the course paid off. The biggest lesson I carry from it: in a production LLM system, the model is the smallest, most swappable part. The trust lives in the data shapes, the grounding, the evaluation, and the traces around it. I'm now excited to bring these concepts into real-world AI systems and extend them across a broader range of ML and data engineering work. A huge thank you, Maria and Başak, for the incredible work and the experience. It genuinely raised the bar for how I think about building. Highly recommend it. For anyone in my network who's interested: https://lnkd.in/dAUr2QRr #LLMOps #Databricks #MLflow #AIEngineering #ContinuousLearning

Most Data Scientists know how to build models. Almost no one knows how to deploy them. 🚀 Just gave a presentation on End-to-End MLOps Pipeline in my Data Science class! Most Data Scientists focus only on building models. But what happens after the model is trained? In this presentation, I covered: ✅ The real problems every ML team faces in production 🐳 How Docker solves the “it works on my machine” problem ⚙️ How GitHub Actions automates the entire deployment pipeline 🔄 How one git push takes a model from code to production automatically MLOps is no longer optional, it is what separates a Data Scientist who experiments from one who ships real products. Swipe through to see the full presentation 👉 #MLOps #Docker #DataScience #MachineLearning #GitHub #AI

🚀 From a notebook to a deployed, monitored ML system—this playlist made it possible. I just finished Vikash Das's MLOps playlist, and it completely changed how I think about shipping ML to production. Like many, I knew how to build models. But making them reliable, reproducible, and ready for the real world? That felt like a different skill entirely. This playlist bridged that gap for me. 📌 Here’s what I actually built (and you can too): > A complete vehicle insurance ML project > Versioned data & code using DVC + Git > Experiment tracking with MLflow > CI/CD pipelines > Docker + FastAPI for serving predictions > Deployment on Kubernetes > Monitoring with Prometheus & Grafana > All integrated with AWS S3 🔗 GitHub repo with my complete work: https://lnkd.in/gHm4HwGM If you're serious about moving from “model works on my laptop” to production-ready ML systems, this is one of the most practical, end-to-end resources I’ve come across. Thank you, Vikash Das, for putting this together so thoughtfully. 🙌 Playlist link: https://lnkd.in/gzUinQN8 #MLOps #MachineLearning #MLflow #Docker #Kubernetes #DataScience #MLProduction #DevOpsForML

"Models don't create value until they survive production." That single line captures why MLOps has become non-negotiable for any data scientist who wants to ship - not just experiment. Just came across KodeKloud's free "100 Days of MLOps" challenge, and it's structured around exactly that idea: → 100 real-world tasks → 12 tool categories (DVC, Feast, MLflow, FastAPI, Docker, Kubernetes, and more) → Live environments + automated validation → Fully free For anyone building out the engineering muscle behind their data science work, this kind of structured, project-based path is gold. What's the one MLOps tool you wish you'd learned earlier? 🔗 Link in comments. #MLOps #DataScience #MachineLearning #CareerGrowth

Databricks just made context engineering a certifiable discipline. The Context Engineer Associate exam launches in beta at Data + AI Summit, June 15-18 in San Francisco. Onsite attendees get one free attempt (worth $200). Online proctored attempts start after the conference. What's tested isn't prompt engineering. It's the operational layer between an agent and its information environment: system prompts and instruction structuring, Vector Search configuration, memory architectures using Lakebase and MLflow, agent integration via MCP, context-window management, Unity Catalog PII handling, multi-agent workflows. That blueprint is more interesting than the cert itself. Databricks calls it "the industry's first certification for reliable AI agent systems." Whether it actually is the first is debatable, but they're the first major vendor to put a name, a blueprint, and an exam behind the unglamorous half of agent work, the half that decides whether your demo survives contact with production. Context engineering as a discipline isn't new. Practitioners have been calling out the framing for over a year. But until now it's been a vibe, not a credential. Two things follow from that: 1. If you build agents on Databricks, this is the next associate cert to look at after the GenAI Engineer Associate. 2. Even if you'll never sit the exam, the topic list is the clearest publicly-available checklist of what you actually need to get right when shipping agents in production. Tracked at https://lnkd.in/dPxEp7Pp #Databricks #AgenticAI #GenAI #MosaicAI #Certification

🚀 Databricks just launched the industry's first certification in Context Engineering And this is a clear signal of where the AI profession is heading. The premise is simple but powerful: the quality of an AI system depends not just on the model, but on the context it receives. Without solid context engineering, even the most advanced models can produce incomplete, inaccurate, or inconsistent results. The Databricks Certified Context Engineer Associate validates your ability to design that information environment. It covers system prompts, Vector Search, memory architectures with MLflow, tool integration via MCP, governance with Unity Catalog, and empirical evaluation of agent performance. This is not a "prompt writing" cert. This is real AI Agent architecture for production. 📋 Beta available for free at DAIS 2026 (San Francisco, June 15–18) — one attempt per attendee, results in 6–8 weeks, valid for 2 years. Is anyone else working on context engineering in their projects? 🔗 Certification page: https://lnkd.in/eeYdxdqU 🔗 Official blog: https://lnkd.in/edsXnuNq 🔗 Exam guide (PDF): https://lnkd.in/erNkT2WZ #Databricks #ContextEngineering #AIAgents #GenAI #Certification #DAIS2026 #AIArchitecture #LLM

🚀 Big data is intimidating, but I just realized the distinct roles Apache Spark and Vector Databases play in the AI ecosystem. While reading today's headlines on scalable data processing, I took a step back to analyze how these tools actually fit into a modern AI stack. When building out full-stack applications, it's easy to get overwhelmed by buzzwords. Spark is an absolute beast for processing terabytes of tabular data in parallel. But for GenAI features, Vector DBs (like Pinecone or Qdrant) are what actually power the fast semantic search by storing high-dimensional embeddings. As I continue building out my own AI SaaS projects, my next technical hurdle is figuring out how to efficiently preprocess raw text data before embedding it into the vector space, without bottlenecking the local environment. For the senior data engineers out there: when building AI pipelines from scratch, do you prefer to handle your data transformation directly in Node.js/Python scripts, or do you spin up lightweight Spark clusters even for smaller datasets? #ApacheSpark #VectorDatabases #DataScience #BuildInPublic #AIArchitecture

Excited to share a machine learning project my team and I just wrapped up! We built a Hospital Appointment No-Show Predictor — a system that flags high-risk patients before appointment day, helping clinics target their intervention efforts where it actually matters. What we built: Trained an XGBoost classifier on 110,527 real clinic records from Brazil Engineered 17 features across temporal, demographic, behavioral, and geographic dimensions Achieved AUC-ROC of 0.751 and 79.2% recall — catching 4 in 5 actual no-shows Deployed as a Flask API on AWS Elastic Beanstalk via Docker, with CI/CD through GitHub Actions Key insight: Lead time (days between booking and appointment) drove 56.3% of feature importance. Patients booked 31–60 days out had a 24.3% no-show rate vs. 15.8% for same-day bookings. Tech stack: Python · XGBoost · Optuna · MLflow · Flask · Docker · AWS Elastic Beanstalk This was a great exercise in building an end-to-end ML pipeline — from raw data and feature engineering all the way to a live deployed API. Huge shoutout to my teammate Muhammad R. and Syed Hashim for making this one a great collaboration! GitHub: https://lnkd.in/dMCCTi7j #MachineLearning #HealthcareAI #XGBoost #MLOps #Python #AWS #DataScience