Learned new skills at MLOps Denver meetup

Databricks is currently offering a 50% discount on certification exams for participants who complete any self-paced learning pathway during the Virtual Learning Festival, which runs from October 10 to 31, 2025. Eligible users will receive their voucher and can also enjoy 20% off an annual Databricks Academy Labs subscription. This initiative is an excellent opportunity to boost data engineering, analytics, or machine learning skills at a reduced cost.

Exploring MLflow Experiments in Databricks 🚀 Today, I ran my first complete MLflow experiment in Databricks — and it really helped me understand how the end-to-end model lifecycle works in practice. Here’s what I observed 👇 🔹 Step 1: I trained a machine learning model using my dataset while MLflow automatically tracked parameters, metrics, and artifacts. 🔹 Step 2: After achieving the desired accuracy, I registered the best model in the Databricks Model Registry. 🔹 Step 3: The registered model can then be deployed in Databricks Apps (or through endpoints) to answer real-time user data queries. It’s fascinating to see how Databricks and MLflow work together — from experiment tracking → model registration → real-time inference — all within one platform. Every experiment teaches something new, and this one made me appreciate how seamless the MLOps workflow can be in Databricks. 💡 #Databricks #MLflow #MLOps #MachineLearning #DataEngineering #DataScience #LearningJourney

The secret weapon of production-grade ML? The Pipeline. If your brilliant model is stuck in a Google colab notebook, a reliable pipeline is what you're missing. It’s the difference between an experiment and a scalable product. I've standardized my workflow around this architecture. (See the diagram in the next swipe ➡️) Here’s why it's non-negotiable for MLOps: •Reproducibility: Every single run is consistent and auditable. •Automation: Data Prep → Training → Deployment on a schedule. Zero manual effort. •Monitoring: Catches data and model drift before it impacts users. •My biggest lesson: Don't skip the Data Validation step. Garbage in, garbage out. A model can be perfect, but bad data will kill it every time. What's the one step in this pipeline that always gives you trouble? Share your MLOps tool of choice! 👇 #MLOps #MachineLearning #DataScience #DataPipeline #TechCareer

🎉 Release 5.3.0 is out! 📚 With this release, you can view all your historical Artifacts in the Artifacts Library. 📊 We've also added support for Connectors with the popular data warehouse Amazon Redshift. 🟢 Finally, keep track of your active Chats with the MOSTLY AI Assistant directly in the left-side navigation menu. 👉 Log on to MOSTLY AI today and let us know you think!

Ever wondered how a data engineer with zero ML background can get started with machine learning?   In this article, our Tech Lead Stefan Mihajlovic shared his step-by-step journey: using Microsoft Fabric and Azure ML Studio to go from raw data to a functional model predicting delivery times. Along the way, he applied the medallion architecture, tested different approaches (from minimal preprocessing to heavy feature engineering), and discovered that sometimes the simplest path delivers the best results.   Check it out if you’re curious about: ✔️ What AutoML looks like in practice ✔️ How Fabric and ML Studio work together ✔️ Setting up real-time endpoints and retraining pipelines   Read the full story and on our website and see if these tools are really ready to bring ML into everyday data engineering! The link is here: https://lnkd.in/dF489Wcp

🚀 Excited to share my latest end-to-end Machine Learning project: Customer Churn Prediction System 📊 This project goes beyond model training — I focused on building a production-ready pipeline that demonstrates the complete ML lifecycle: ✅ Data preprocessing, feature engineering, and EDA ✅ Model training with hyperparameter tuning (Random Forests) ✅ Deployment as an interactive Streamlit app ✅ Containerization with Docker for easy portability ✅ Automated linting & testing with GitHub Actions (CI/CD) The goal was not only to predict churn but also to design a scalable, reproducible, and deployable ML solution — bridging the gap between data science and MLOps. 🔗 Check out the repo here: https://lnkd.in/dDvGRXvk Always open to feedback, collaborations, and discussions on improving real-world ML workflows! 🌟 #DataScience #MachineLearning #MLOps #Streamlit #Docker #GitHubActions #ChurnPrediction

Check out how OpenAI and its Finance and Engineering team is using Databricks in its new DocuGPT solution for contract workflows and analytics (at 1:15 in the video 👏 ). https://lnkd.in/gYt3xdMq

Databricks certifications usually cost around $200. If you're looking to learn something in Data or AI and get certified, you can now get 50% off by completing any of their self-paced learning paths between Oct 10–31. Great chance to upskill without breaking the bank!

A look back at one of my learning experiences in 2024🌟 I had the opportunity to attend an international webinar by Koenig Solutions on “MLflow – One Stop Solution for Data Scientist.” It inspired me to explore more about MLOps and efficient ML experiment tracking. During this international webinar, I learned how MLflow serves as an all-in-one platform for managing the entire machine learning lifecycle — from experiment tracking and model management to deployment. The session provided valuable insights into implementing MLOps best practices, improving experiment reproducibility, and streamlining collaboration within data science teams #MLflow #DataScience #MLOps #LearningJourney #KoenigSolutions

When we think about AI competition, it is easy to assume the winners will be decided by who has the biggest model. But as MongoDB's Scott Sanchez and Pete Johnson explain in The New Stack, the real battle is being fought in go-to-market strategies. From pricing models to safety guardrails, execution choices are defining the industry just as much as new technology. Check out the article for their analysis and three key insights. 👇 https://lnkd.in/dFyfYarX