Overview

Build and Deploy Intelligent Machine Learning Solutions Using Microsoft AzureThis course is your complete guide to mastering data science workflows in the cloud. Designed for professionals who want to go beyond experimentation and take their machine learning models into production, it covers every stage of the ML lifecycle using Azure's powerful suite of tools.Whether you're looking to scale your data science capabilities, prepare for the DP-100 certification, or enhance your organization's AI capabilities, this course delivers hands-on experience with the platforms and practices used in real-world enterprise environments.You will gain hands-on expertise in:Designing effective ML architectures on AzureChoosing the right dataset formats and compute targetsStructuring experiments for scalability and performanceIntegrating Git and CI/CD pipelines for streamlined collaborationPreparing and managing data at scaleWrangling and transforming data using notebooks and Synapse SparkAccessing and versioning datasets via Azure ML datastoresBuilding and sharing environments across workspacesTraining models using both automated and custom approachesLeveraging AutoML for classification, regression, vision, and NLPDeveloping custom training scripts using Python and MLflowTuning hyperparameters for optimal model performanceBuilding and managing reproducible ML pipelinesCreating modular training componentsPassing and transforming data between pipeline stepsScheduling, monitoring, and debugging workflowsDeploying models for real-time and batch inferenceConfiguring online endpoints for scalable predictionsSetting up batch endpoints for large-scale processing jobsImplementing secure and compliant deployment workflowsOptimizing advanced AI models and LLMsSelecting and fine-tuning large language modelsDesigning prompt engineering strategies for accuracy and contextImplementing Retrieval Augmented Generation (RAG) systemsEnsuring responsible AI and operational excellenceApplying fairness, transparency, and explainability principlesUsing MLflow for experiment tracking and model governanceAutomating retraining and monitoring in productionIf you're ready to move beyond theory and start building machine learning systems that solve real business problems, this course is designed for you. It's perfect for learners who want structured guidance, practical tools, and hands-on labs that mirror what professionals do in industry every day.

Skills

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