DP 100T01 Designing and Implementing a Data Science Solution on Azure Course
- Duration: 32 Hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The DP-100T01: Designing and Implementing a Data Science Solution on Azure Course delivers comprehensive training for professionals responsible for building and operationalizing machine learning solutions in Azure. Participants gain a structured understanding of the data science lifecycle—from data preparation and feature engineering to model training, evaluation, deployment, and monitoring. The course emphasizes reproducibility, scalability, and collaboration, enabling learners to move from experimentation to production-ready solutions using Azure-native tools and best practices.
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Course Details
- Build practical skills in end-to-end data science solution development on Azure.
- Learn how to prepare data and engineer features for machine learning.
- Train, evaluate, and compare models using structured experimentation.
- Deploy models as scalable services and monitor performance.
- Apply MLOps concepts for repeatable, reliable workflows.
- Establish a strong foundation for advanced Azure data science and ML roles.
- Ideal for data scientists, machine learning engineers, and analytics professionals.
- Recommended to have basic knowledge of Python and data science concepts.
- Familiarity with statistics and machine learning fundamentals is beneficial.
- No prerequisite certifications required for course participation.
- Design data science solutions aligned to business requirements.
- Prepare and explore data for machine learning workloads.
- Train, evaluate, and tune machine learning models.
- Track experiments and manage model versions.
- Deploy models and monitor performance in production.
- Implement MLOps practices for collaboration and governance.
- Azure Data Science Overview: Solution architecture and lifecycle.
- Data Preparation and Exploration: Cleaning, transformation, and EDA.
- Feature Engineering: Techniques to improve model performance.
- Model Training and Evaluation: Metrics, validation, and comparison.
- Experiment Tracking: Reproducibility and collaboration concepts.
- Model Deployment: Endpoints, scaling, and inference.
- Monitoring and MLOps: Performance, drift, and operational best practices.
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