Designing and Implementing an Azure Data Science Solution (DP-100)
Designing and Implementing an Azure Data Science Solution (DP-100) is a offering by Microsoft that focuses on using Azure services and tools to create, manage, and deploy data science solutions.
- Azure Machine Learning Workspace: Set up and manage environments for data science projects.
- Data Preparation: Use Azure tools for data ingestion, transformation, and cleaning.
- Model Development: Train and evaluate machine learning models using Azure services.
- Model Deployment: Deploy models for production use within Azure environments.
- Automation: Implement machine learning pipelines and automate workflows.
- Basic Understanding of Machine Learning: Familiarity with concepts like model training, evaluation, and deployment.
- Experience with Python/R: Proficiency in using Python or R for data analysis and model development.
- Data Handling Skills: Knowledge of data preprocessing, cleaning, and transformation techniques.
- Azure Fundamentals: Basic understanding of Azure services and cloud computing concepts.
- Azure Machine Learning Expertise: Ability to create, train, and deploy machine learning models using Azure Machine Learning service.
- Data Pipeline Development: Skills in designing and implementing data pipelines for data ingestion, transformation, and analysis.
- Model Management: Proficiency in managing, monitoring, and optimizing machine learning models in production.
- End-to-End Solution Design: Capability to design and implement end-to-end data science solutions on Azure, from data preparation to model deployment.
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