Training Overview
Disclaimer: This course is independently developed and not affiliated with Microsoft. It covers concepts and skills that closely align with the objectives of Microsoft’s DP-100: Designing and implementing a data science solution on Azure training, making it a strong preparatory or complementary learning experience.
Azure Data Scientist Mastery: Building Intelligent Models and Scalable ML Workflows in the Cloud
This four-day advanced custom training equips participants with the skills to design, implement, and operationalize machine learning solutions using Azure Machine Learning and MLflow. By the end of the training, learners will be able to build reproducible ML workflows for tasks such as classification, regression, clustering, and deep learning; optimize models through feature engineering, hyperparameter tuning, and scalable pipelines; deploy and monitor ML solutions in cloud-native environments; and integrate generative AI capabilities using Azure AI services.
This training, is ideal for data scientists, Analyst, AI, ML & Data engineers, to quickly gain the most value from it. This intermediate-level course emphasizes hands-on proficiency across the full machine learning lifecycle, from data ingestion to production deployment.
Module Breakdown:
Explore and Configure Azure ML Workspace: Set up the foundational environment for ML workloads. Learn to create workspaces, manage compute targets, and configure data storage for scalable experimentation
Experiment with Azure Machine Learning: Dive into data exploration and experimentation. Use notebooks and SDKs to run training jobs, log metrics, and visualize results for iterative model development
Optimize Model Training: Focus on performance tuning. Apply automated ML, hyperparameter sweeps, and pipeline orchestration to refine model accuracy and efficiency
Manage and Review Models: Track model versions, evaluate performance, and register models for deployment. Emphasize reproducibility and governance using MLflow and Azure ML tools
Deploy and Consume Models: Operationalize your models. Learn to deploy endpoints, integrate with REST APIs, and monitor real-time predictions in production environments
Develop Generative AI Apps in Azure: Extend your ML solutions with Azure AI services. Build apps using Azure AI Foundry, integrate language models, and optimize for conversational and generative use cases