AZ MACHINE LEARNING
Mastering Azure Machine Learning
AZ MACHINE LEARNING
Azure Machine Learning is a cloud-based service provided by Microsoft that enables developers and data scientists to build, deploy, and manage machine learning models at scale. It offers a comprehensive suite of tools and services for various stages of the machine learning lifecycle, including data preparation, model training, evaluation, and deployment. Azure Machine Learning supports both code-first approaches using Python and R, as well as low-code options through a visual interface, making it accessible to users with varying levels of expertise. The platform integrates seamlessly with other Azure services, providing robust infrastructure, scalability, and advanced capabilities such as automated machine learning (AutoML), neural architecture search, and support for popular frameworks like TensorFlow and PyTorch. With built-in features for model management, versioning, and monitoring, Azure Machine Learning facilitates collaborative workflows and ensures that machine learning solutions can be easily operationalized and maintained.
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1 - Introduction to Azure Machine Learning: Overview of AzureML, its purpose, and its relevance in the field of machine learning and data science.
2) Cloud Based Platform: Understanding how AzureML operates in the cloud, providing scalability, on demand resources, and support for distributed computing.
3) Workspace and Resources: Learn about creating and managing an AzureML Workspace, a central place to organize all your machine learning assets.
4) Data Preparation and Ingestion: Techniques for collecting, cleaning, and preparing data for machine learning models using Azure’s data services.
5) Exploratory Data Analysis (EDA): Performing EDA within AzureML to visualize data distributions, correlations, and insights through integrated tools.
6) Model Training: Learn how to build different types of machine learning models using AzureML’s built in algorithms and frameworks like Scikit learn, TensorFlow, and PyTorch.
7) AutoML Capabilities: Exploration of Azure’s Automated Machine Learning feature, which helps automate model selection and hyperparameter tuning.
8) Experimentation and Tracking: Utilizing AzureML's experimentation capabilities to track model performance, metrics, and parameters in a systematic way.
9) Model Deployment: Steps for deploying trained models as web services for real time predictions, using Azure’s robust infrastructure.
10) Scaling Models with MLOps: Introduction to MLOps (Machine Learning Operations) practices for model management, continuous integration, and deployment in Azure.
11) Use of Notebooks: Familiarization with Azure Notebooks and Jupyter for interactive coding and model development within AzureML.
12) Integration with Other Azure Services: Explore how AzureML can integrate with other Azure services like Azure Data Lake, Azure SQL, and Azure Functions for enhanced functionality.
13) Security and Compliance: Understanding Azure’s security measures, data governance, and compliance features to protect sensitive data in machine learning workflows.
14) Cost Management: Learning about pricing models for using AzureML effectively and strategies for optimizing resource usage.
15) Real World Applications and Case Studies: Analyzing real world applications of Azure Machine Learning in various industries such as finance, healthcare, and retail.
16) Best Practices: Discussing best practices for machine learning projects, including data versioning, model interpretability, and documentation.
17) Hands On Projects: Engaging students in practical hands on projects that allow them to apply skills learned throughout the training program.
18) Future Trends in AI and Machine Learning: Exploring emerging trends and technologies in AI and machine learning that are facilitated by platforms like Azure.
19) Support and Community Resources: Guide students on utilizing Microsoft’s support resources, forums, and community channels to continue their learning journey beyond the training program.
20) Certification Preparation: Information on relevant Azure certifications, such as the Azure Data Scientist Associate, to help students validate their skills in the job market.
This structure provides a comprehensive training program that covers foundational knowledge and hands on experience with Azure Machine Learning, preparing students for future challenges in data science and machine learning fields.
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