To ensure a successful learning experience in the DP-100T01: Designing and Implementing a Data Science Solution on Azure course, participants should have the following minimum prerequisites:

• Basic understanding of data science and machine learning concepts.
• Familiarity with common data science processes such as data exploration, data cleaning, feature engineering, model training, and evaluation.
• Experience with Python programming, as Python is frequently used for data manipulation and model training within Azure Machine Learning.
• Exposure to basic statistics, as they form the foundation of many machine learning algorithms.
• Knowledge of cloud computing fundamentals, particularly within the Microsoft Azure ecosystem.
• Prior experience using Azure services is beneficial but not mandatory.

These prerequisites are designed to provide a baseline for course participants, ensuring that they can actively engage with the course material and practical labs.

Target Audience for DP-100T01: Designing and Implementing a Data Science Solution on Azure

The DP-100T01 course is designed for professionals seeking to implement data science solutions on Azure's cloud platform.

• Data Scientists
• AI Engineers
• Machine Learning Engineers
• Cloud Solutions Architects
• IT Professionals with a focus on data analytics
• Software Developers interested in data science and machine learning
• Technical Leads managing data science teams
• Data Analysts aiming to advance in machine learning
• DevOps Engineers focused on ML/AI lifecycle management
• Professionals preparing for Azure Data Scientist Associate certification

Learning Objectives - What you will Learn in this DP-100T01: Designing and Implementing a Data Science Solution on Azure?

Introduction to the Course's Learning Outcomes and Concepts Covered:

The DP-100T01: Designing and Implementing a Data Science Solution on Azure course provides a comprehensive understanding of how to leverage Azure Machine Learning for building, training, and deploying predictive models.

Learning Objectives and Outcomes:

• Create and Configure an Azure Machine Learning Workspace: Understand how to set up and manage the workspace, including assets and tools for machine learning projects.
• Utilize Azure Machine Learning Tools: Learn to use both the Azure Machine Learning Studio and the Python SDK for machine learning tasks.
• Run Automated Machine Learning Experiments: Discover how to use Automated ML to quickly identify high-performing models.
• Build and Publish Models Using Designer: Explore the no-code Designer interface to train and deploy models without writing code.
• Execute and Track Machine Learning Experiments: Learn to run experiments, track metrics, and register models within Azure Machine Learning.
• Optimize Model Training with Hyperparameter Tuning: Utilize Azure Machine Learning's capabilities to fine-tune model performance.
• Deploy Models for Real-time and Batch Inferencing: Master the deployment process for both real-time and batch predictions in Azure.
• Create and Manage End-to-End Machine Learning Pipelines: Learn to orchestrate machine learning workflows with pipelines for reproducibility and scalability.
• Interpret and Explain Models for Accountability: Gain insights into model behavior and ensure responsible AI with interpretability and fairness tools.
• Monitor Models and Data Drift in Production: Implement monitoring for model performance and data drift to maintain and improve model reliability over time.