Streams, Lakes and Oceans - Working with Big Data with Azure ML

Overview

Explore big data architectures with Azure ML Studio, covering integration, customization, and scaling for effective machine learning on large datasets.

Syllabus

• Introduction to Big Data and Azure Machine Learning
Overview of big data concepts
Introduction to Azure ML Studio
Key features and advantages of using Azure in big data environments
• Understanding Azure ML Studio Interface
Navigating the Azure ML Studio environment
Key tools and panes in Azure ML
• Big Data Architectures on Azure
Difference between streams, lakes, and oceans in big data
Data Lake Storage and Azure Stream Analytics
Integrating Big Data tools with Azure ML
• Data Preparation and Cleaning
Importing data into Azure ML
Data cleaning techniques
Handling missing data and outliers in large datasets
• Machine Learning Model Development
Choosing the right ML models for big data
Training and testing models in Azure ML
Model evaluation metrics for big data applications
• Scaling Machine Learning Workloads
Parallel processing and distributed computing
Scaling computations with Azure ML
Optimizing performance for large datasets
• Customizing and Automating Workflows
Creating custom modules in Azure ML
Building automated workflows with Azure ML Pipelines
Experimentation and iteration with large datasets
• Integrating Azure ML with Other Azure Services
Using Azure Data Factory for data movement
Real-time analytics with Azure Stream Analytics
Integration with Azure Databricks for enhanced analytics
• Case Studies and Real-world Applications
Successful big data projects using Azure ML
Best practices in industry-specific applications
• Ethical Considerations and Future Trends
Data privacy concerns in big data
Emerging trends in big data and machine learning
• Course Conclusion and Final Project
Recap of key learning points
Final project to demonstrate integration and scalability
Resources for further learning and certification paths

Subjects

Conference Talks