Learn to productionize machine learning pipelines using Databricks and Azure ML in this 49-minute tutorial. Discover a reproducible framework for quickly launching data science projects that enables easy production-ready app deployment. Explore sample code, templates, and recommended project organization structures while gaining insights into deploying machine learning pipelines. Develop pipelines for continuous integration and deployment within Azure Machine Learning using Azure Databricks. Execute Apache Spark jobs with Databricks Connect and integrate source code with Azure DevOps for version control. Gain hands-on experience building deep learning models for image classification using TensorFlow and PyTorch. Address ML lifecycle challenges by implementing MLflow to track model parameters, results, package code for reproducibility, and deploy models. Prerequisites include an Azure account, configured workspaces, Databricks trial, Docker installation, and basic knowledge of Python, Spark, and deep learning concepts. Read more