Azure Machine Learning (AML) Training in India

This course introduces participants to Azure Machine Learning (Azure ML), Microsoft's cloud-based machine learning service. It covers the end-to-end ML lifecycle including data preparation, model training, evaluation, and deployment using Azure ML Studio and SDKs. The course balances conceptual learning with hands-on labs to provide practical exposure to building scalable ML workflows in the cloud.

Welcome to the official Azure Machine Learning (AML) Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in India. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic knowledge of Machine Learning concepts

• Familiarity with Python programming

• Prior experience with data analysis tools (e.g., Pandas, NumPy)

• Understanding of Azure fundamentals is helpful but not mandatory

• Azure Machine Learning Studio & SDK usage

• Cloud-based model training and evaluation

• Automated Machine Learning (AutoML)

• Building and managing ML pipelines

• Deployment using Azure Kubernetes Services (AKS) or Azure Container Instances (ACI)

• Integration with Jupyter Notebooks and VS Code in Azure

• Model interpretability and performance tracking

Upon completion of this course, participants will be able to:

• Navigate and utilize Azure Machine Learning Studio and Workspaces

• Prepare data and create ML pipelines in Azure

• Train and evaluate models using Azure AutoML and custom Python scripts

• Deploy and manage models as web services in Azure

• Monitor model performance and retrain as necessary