Microsoft Azure Machine Learning Studio simplifies this process by providing a cloud-based platform to build, train, and deploy machine learning models efficiently. This course is designed to help learners master Azure ML Studio through a structured, hands-on approach. This course covers the entire machine learning lifecycle, from understanding key concepts to deploying models in production environments. Learners will explore: Types of Machine Learning – Supervised, unsupervised, and reinforcement learning.Real-world applications in healthcare, finance, cybersecurity, and retail.Challenges in Machine Learning – Overfitting, data quality, interpretability, and scalability. Hands-on with Azure ML Studio Through practical demonstrations, learners will: Navigate the Azure Machine Learning Studio interface and set up a workspace.Manage datasets, experiments, and models in a cloud-based environment.Preprocess data – Handle missing values, perform feature engineering, and split datasets for training.Use data transformation techniques – Standardization, normalization, one-hot encoding, and PCA. Building & Training Machine Learning Models Learners will explore different machine learning algorithms and techniques, including: Regression, classification, and clustering models in Azure ML Studio.Feature selection and hyperparameter tuning for better model performance.AutoML (Automated Machine Learning) for optimizing models with minimal effort.Ensemble learning methods such as Random Forests, Gradient Boosting, and Neural Networks. Model Deployment & Optimization Once models are trained, learners will dive into model deployment strategies: Real-time inference vs. batch inference using Azure Kubernetes Service (AKS) and Azure Functions.Security best practices – Role-Based Access Control (RBAC), compliance, and encryption.Monitoring model drift – Implementing tracking tools to detect performance degradation over time. Automating Machine Learning Workflows This course includes Azure ML Pipelines to automate machine learning processes: Building end-to-end pipelines – Automate data ingestion, model training, and evaluation.Using custom Python scripts in ML pipelines.Monitoring and managing pipeline execution for scalability and efficiency. MLOps & CI/CD for Machine Learning Learners will gain practical knowledge of MLOps and CI/CD for ML models using: Azure DevOps & GitHub Actions for model versioning and retraining automation.CI/CD pipelines for seamless ML model updates.Techniques for model lifecycle management – Deployment, monitoring, and rollback strategies. Exploring Generative AI with Azure ML This course also introduces Generative AI: Working with Azure OpenAI Services – GPT, DALL·E, and Codex.Fine-tuning AI models for domain-specific applications.Ethical AI considerations – Bias detection, explainability, and responsible AI practices.Microsoft Certified: Azure Data Scientist Associate -  DP-100Prepare for Microsoft Certified: Azure AI Engineer Associate -  AI-102 Who this course is for: If you’re new to ML and want a structured, hands-on introduction using Azure Machine Learning Studio, this course will provide step-by-step guidance.Learners preparing for Exam AI-102: Microsoft Certified: Azure AI Engineer AssociateIf you have some knowledge of ML but want to scale your models using Azure’s cloud-based ML tools, this course will help you learn model training, deployment, and automation.If you work with data and want to transition into machine learning and AI, this course will teach you how to build, optimize, and deploy ML models efficiently in Azure ML Studio.you're an Azure user, cloud engineer, or solutions architect, this course will teach you how to integrate Azure ML with cloud-based services for AI-driven solutions.If you’re a software developer or Python programmer looking to automate machine learning workflows and deploy AI solutions, this course will provide the skills you need.If you're interested in MLOps, CI/CD for ML models, and automated retraining, this course covers end-to-end model lifecycle management in Azure ML.If you work in healthcare, finance, retail, cybersecurity, or any data-driven industry, this course will show you how machine learning can solve real-world business problems.