🚀 Learn Databricks for AI Engineering with a complete hands-on tutorial!

In this video, you'll build an end-to-end machine learning workflow using Databricks Free Edition. Starting with a simple machine learning model, you'll learn how to track experiments with MLflow, register and version models using the Databricks Model Registry, deploy models using Managed Model Serving, and invoke them through REST APIs. Whether you're preparing for an AI Engineering role, learning MLOps, or exploring Databricks for the first time, this tutorial demonstrates the complete enterprise machine learning lifecycle. 📚 What You'll Learn ✅ Create and navigate a Databricks workspace ✅ Use the built-in Databricks AI Assistant ✅ Build a Machine Learning model in a Databricks Notebook ✅ Track experiments using integrated MLflow ✅ Compare multiple experiment runs ✅ Register and version machine learning models ✅ Deploy models as Managed Serving Endpoints ✅ Test deployed models using the Query Interface ✅ Monitor serving endpoints ✅ Update production endpoints with newer model versions 🔗 Resources 📂 GitHub Dataset: https://github.com/futurexskill/ml-mo... Notebook https://github.com/futurexskill/ml-mo... Sample Input JSON for Model Serving { "dataframe_records": [ { "Age": 35, "Salary": 60000 } ] } 🚀 *Interested in building real-world AI applications?* Check out the *AI Engineering: Model Deployment, MLOps & Agentic AI* course on Udemy. Starting with Machine Learning model deployment and MLOps, the course progresses through LLMs, Generative AI, and Agentic AI with practical, hands-on projects and is continuously updated with the latest AI technologies. https://www.udemy.com/course/machine-...