Certification in AZURE ML

Azure ML provides a comprehensive data management framework that ingests, stores, pre-processes, and accesses data with versioning, profiling, and drift monitoring across the lifecycle, ensuring security and compliance.

Explore Azure ML data storage options, from blob storage to data lake and Cosmos DB, and learn lifecycle policies, tiers, and integration for scalable ML workflows.

Learn how to prepare data and engineer features in Azure ML Studio by registering datasets, managing data assets, and using the feature store for reusable features across projects.

Azure Data Lake enables scalable big data analytics for machine learning by integrating with Azure Synapse Analytics and Azure Databricks, backed by Data Lake Storage Gen2.

Learn how to import and manage datasets in Azure ML with data versioning, governance, and data lineage, and enforce access control using RBAC while registering new dataset versions.

Secure data access in Azure ML with authentication via AAD and managed identity, protect data with server side and client side encryption, and isolate traffic using private endpoints in VNet.

Upload datasets to Azure blob storage, register them as tabular data in Azure Machine Learning, and prep them with the no-code Azure ML designer.

Effective data management in Azure Machine Learning provides a robust foundation for the ML lifecycle, with data stores, datasets, and data frames ensuring versioning, governance, and reproducibility.

Build and train scalable models on Azure ML using low-code, AutoML, and flexible frameworks like scikit-learn, TensorFlow, and PyTorch, with cloud compute for distributed training.

Azure ML designer enables no-code ML development with a drag-and-drop interface to build, train, evaluate, and deploy end-to-end pipelines using pre-built modules, data integration, and one-click REST API deployment.

Explore code-based model development in Azure ML using Jupyter notebooks and the Azure ML SDK, from workspace setup and compute provisioning to registering datasets and training orchestration.

Explore how Azure ML automates feature engineering, model selection, and hyperparameter tuning with AutoML, using classification tasks and ten iterations to identify the best model.

Train a custom model on Azure ML compute instances using train.py, submit as an experiment, and use hyperdrive to optimize; then evaluate, log, and deploy via an ACI service.

Train with Azure ML compute clusters and hyperdrive to scale, monitor experiments, enable data versioning, and select VM and GPU configurations for churn, fraud, and recommendations.

Train and deploy the best Azure AutoML model in Azure ML Studio, and in parallel build a custom Python model with the Azure ML SDK.

Explore how Azure Machine Learning streamlines building and training models at scale, from AutoML and custom scripts to distributed training, experiment tracking, and secure deployment as APIs.

Optimize and evaluate models in Azure ML by Hyperdrive tuning, feature selection, and regularization, then assess performance with metrics like accuracy, precision, recall, and AUC.

Explore hyperparameter tuning with Azure ML Hyperdrive to automatically search learning rate, batch size, and layers using random, grid, and Bayesian optimization, boosting model accuracy and best model registration.

Learn to evaluate classification and regression models using metrics like accuracy, precision, recall, F1, AUC ROC, MAE, MSE, RMSE, and R-squared in Azure ML.

Develop robust models with cross-validation and model selection, leveraging feature engineering, PCA and recursive feature elimination, scaling and encoding of categorical variables for better generalization.

Explore model explainability with Azure interpretability toolkit, Shap, and Lime, and deploy optimized Azure ML models via rest APIs. Learn best practices like pruning, quantization, ONNX, hyperparameter tuning, and monitoring.

Use Azure ML hyperdrive to tune hyperparameters with random or grid sampling, log metrics, and monitor runs in Azure ML Studio to identify the best model for deployment.

Explore the conclusion and next steps of model optimization and evaluation in Azure Machine Learning, detailing hyperparameter tuning with hyperdrive, multi-metric evaluation, interpretability tools, model compression, and deployment.

Learn to deploy machine learning models with Azure ML across ACI, AKS, managed online endpoints, and batch endpoints. Expose predictions via secure REST APIs with monitoring and CI/CD.

Register trained models and create Azure ML inference pipelines to deploy them on a unified endpoint with routing. Define the runtime through score.py and environment.yml to enable robust inference.

Explore real-time versus batch inference in Azure Machine Learning, focusing on batch endpoints for large-scale asynchronous predictions, deployment steps, and use cases like fraud detection and churn analysis.

Master Azure ML managed online endpoints for endpoint configuration and authentication with auto scaling and real-time inference. Test deployments, monitor via App Insights, and manage resources with logs and cleanup.

Deploy a trained ML model to an Azure ML endpoint as a real-time REST API. Register the model, create an inference script, and deploy to ACI or AKS.

Deploy machine learning models with Azure ML to production via real time endpoints, batch inference, or managed online endpoints, with MLOps for monitoring.

Explore how Azure ML integrates with analytics, monitoring, automation, and cognitive services to enable end-to-end machine learning from data ingestion to deployment and real-time decisions.

Explore an end-to-end data analytics and visualization pipeline with Azure Synapse and Power BI, from raw data ingestion and ML model scoring to interactive dashboards and real-time insights.

Monitor and log deployed machine learning models with Azure Monitor to track latency, throughput, and error rates. Detect data drift, trigger retraining, and surface alerts with dashboards for real-time visibility.

Automate and orchestrate ml workflows with azure logic apps using pre-built connectors to azure services and apps, triggering retraining, updating Power BI dashboards, and notifying teams when new data arrives.

Integrate Azure ML predictions with Power BI to build interactive dashboards that visualize predictions, actual values, and KPI insights, with real-time refresh and secure data connections.

Integrate Azure ML with Azure Data Lake, Azure Synapse Analytics, Power BI, and Azure DevOps to streamline the machine learning lifecycle from data ingestion to deployment and monitoring.

Explore mlops and workflow automation that integrate devops with machine learning to automate data ingestion, feature engineering, training, deployment, and monitoring within the Azure ML ecosystem.

Understand how MLOps merges machine learning and DevOps to automate the full ML lifecycle, including data ingestion, training, deployment, and monitoring, through CI/CD pipelines using Kubeflow, MLflow, and Azure ML.

Automate the full machine learning lifecycle with Azure ML pipelines, deploying trained models to production via ci/cd, with data drift and concept drift triggers for retraining.

Explore how Azure machine learning manages the full model lifecycle from development to deployment, with automatic versioning in the model registry, staged testing, production endpoints, monitoring, retraining, and governance.

Monitor deployed models and trigger automated retraining in production with MLflow pipelines. Retrain when accuracy falls below 0.85, addressing data drift and concept drift.

Automate the full machine learning workflow from data preparation to deployment with azure devops and azure ml. Build ci/cd pipelines, validate data, train models, and deploy to staging or production.

Master MLOps and workflow automation to accelerate production-ready, scalable, and reliable machine learning through end-to-end pipelines, automated training, deployment, monitoring, and CI/CD using Azure Machine Learning and other platforms.

Explore how Azure ML secures data, ensures GDPR, Hypa, and ISO compliance, and optimizes costs with cost management, auto scaling, and job scheduling for scalable ML deployments.

Explore data security in Azure ML with role-based access control. Implement Azure AD identity management, managed identities, and MFA for secure access.

Explore data security in Azure ML through role-based access control and scope. Learn Azure AD integration, managed identities, and MFA to enforce least-privilege access.

Discover how Azure ML aligns with ISO 27001, SOC 2, GDPR, HIPAA, and FedRAMP, and implement governance, policy, and audit logging for compliant machine learning.

Explore cost optimization strategies for Azure ML workloads, including right-sizing compute, using spot and low-priority VMs, auto scaling, and selective GPU/TPU use.

Explore Azure ML's pay-as-you-go pricing across the ML lifecycle—data processing, training, deployment, and monitoring—covering compute, storage, deployment costs, per-second billing, and cost management.

Master hands-on RBAC setup in Azure Machine Learning with built-in roles and workspace specific roles, and monitor and optimize costs using Azure Cost Management.

Explore how Azure ML ensures security, compliance, and cost optimization through encryption, Azure AD, rule based access control, private networking, and scalable cost saving strategies.

Explore real world Azure ML use cases across industries, from real time AI and sentiment analysis to fraud detection and churn prediction, with scalable model building, training, deployment, and monitoring.

Explore real time fraud detection and risk management with Azure ML's anomaly detection and AutoML. See how JPMorgan Chase applies predictive models for credit scoring, loan approvals, and fraud prevention.

Explore healthcare predictive analytics and AI-driven diagnostics with Azure ML and Azure AutoML, training and deploying models from multi-source data to forecast disease risks and personalize care.

Explore how retail and e-commerce leverage Azure ML for time series forecasting and inventory optimization to predict product demand, reduce stock outs, and enable personalized recommendations.

Learn how manufacturing predictive maintenance uses IoT data and time series forecasting in Azure Machine Learning to prevent downtime and enable proactive maintenance. Demonstrate AI-powered defect detection with computer vision.

Address a domain-specific problem with Azure ML to solve a real-world challenge in healthcare, finance, retail, or manufacturing by preparing data, training with AutoML or Python, and deploying to endpoint.

Azure machine learning enables real world use cases across manufacturing, finance, retail, and healthcare, including predictive maintenance, fraud detection, and personalized recommendations, with scalable infrastructure and end-to-end workflows.

Learn to build a capstone project end-to-end with Azure ML. Prepare data, train models, deploy, and monitor, applying churn prediction, recommendation, and real-time sentiment analysis with Power BI.