Databricks MLOps with 2 Lines of Code!

This demo covers a full MLOPs pipeline. We'll show you how Databricks Lakehouse can be leverage to orchestrate and deploy model in production while ensuring governance, security and robustness. Ingest data and save them as feature store • Build ML model with Databricks AutoML • Setup MLFlow hook to automatically test our models • Create the model test job • Automatically move model in production once the test are validated • Periodically retrain our model to prevent from drift ☎️ Do you need any career or technical help? Book a call with me: https://calendly.com/mg_cafe Databricks MLOps With GitHub Actions & MLflow: https://youtu.be/f2XQMFod8kg Trigger MLOps Pipelines using Databricks MLflow Webhooks: https://youtu.be/wN4fFtMhwMA Remote Centralized Feature Store in Databricks: https://youtu.be/6OSYG6gzDtY Online Databricks Feature Store for Real-Time Inferencing: https://youtu.be/JH_244dhr6A AutoML Comparison in Databricks VS Azure Machine Learning: https://youtu.be/jf5fKXuLg8s Subscribe for more cloud computing, data, and AI analytics videos by clicking on subscribe button so you don't miss anything. ✅ You can contact me at: LinkedIn: https://www.linkedin.com/in/mohammad-ghodratigohar/ Email: mo.ghodrati95@gmail.com Twitter: https://twitter.com/MG_cafe01 #Databricks #MLOps #MLflow #AzureMLOps #DevOpsForMachineLearning #MAchineLearningOperation #Azure #AI #DevOps #DatabricksMLOps #DatabricksMLflow #Databricks #MLOps #MLflowWebhooks #MLflow #Webhooks #Slack #CICD #AzureMLOps #AzureDevOps #DevOpsForMachineLearning #MAchineLearningOperation #Azure #AI #DevOps #DatabricksMLOps #DatabricksMLflow