DP-100T01: Creating and Putting into Practice an Azure Data Science Solution Course Overview

A thorough examination of Azure’s machine learning capabilities can be found in the course DP-100T01: Designing and Implementing a Data Science Solution on Azure. It covers every step of the data science process, including managing, deploying, training, and preparing data. By using Azure Machine Learning Studio and Azure Machine Learning Service, learners will get hands-on experience and learn how to build, train, optimize, and implement machine learning models at scale. During the course, students will work in hands-on labs where they will create an Azure Machine Learning workspace, conduct experiments, work with datasets and datastores, and use pipelines to orchestrate machine learning workflows. Additionally, they will investigate batch and real-time inferencing to make sure their models can manage large-scale processing or react quickly. Students who grasp automated machine learning, model interpretation, and hyperparameter tuning will be well-prepared to develop ethical AI solutions. They will also discuss how to use tools like Application Insights and data drift monitoring to monitor models and maintain optimal performance over time. This course is perfect for aspiring and practicing data scientists who want to improve and optimize their machine learning workflows by using Azure’s power.

Course Prerequisites

The following minimal requirements should be met by participants in the DP-100T01: Designing and Implementing a Data Science Solution on Azure course in order to guarantee a successful learning experience:

• Fundamental comprehension of machine learning and data science ideas.
• knowledge of standard data science procedures, including feature engineering, data cleansing, data exploration, model training, and evaluation.
• Familiarity with Python programming, since Azure Machine Learning often uses Python for model training and data manipulation.
• Exposure to fundamental statistics, since many machine learning algorithms rely on them.
• Basic understanding of cloud computing, especially as it relates to the Microsoft Azure ecosystem.
• Although not required, prior experience with Azure services is advantageous.

Target Audience for DP-100T01: Creating and Putting into Practice an Azure Data Science Solution

Professionals looking to apply data science solutions on Azure’s cloud platform should take the DP-100T01 course.

• Scientists of Data
• Engineers in AI
• Engineers in Machine Learning
• Architects of Cloud Solutions
• IT specialists who specialize in data analytics
• Software developers with a passion for machine learning and data science
• Technical Leads overseeing teams in data science
• Data analysts who want to become machine learning experts
• DevOps engineers with an emphasis on lifecycle management for ML/AI
• Professionals getting ready to become certified as Azure Data Scientist Associates

Learning Objectives – What is covered in this DP-100T01 course on designing and implementing an Azure data science solution?

Overview of the Learning Objectives and Topics Covered in the Course:

Building, training, and deploying predictive models with Azure Machine Learning is made simple with the help of this course, DP-100T01: Designing and Implementing a Data Science Solution on Azure.

Learning Objectives and Outcomes

• Establish and Configure an Azure Machine Learning Workspace: Learn how to organize and maintain the workspace, along with the resources and equipment needed for machine learning initiatives.
• Make Use of Azure Machine Learning Tools: Acquire the Knowledge to Apply the Python SDK and the Azure Machine Learning Studio to Machine Learning Tasks.
• Conduct Automated Machine Learning Experiments: Learn how to find high-performing models fast with Automated ML.
• Create and Publish Models with Designer: Train and implement models without writing code by navigating through the no-code Designer interface.
• Execute and Monitor Machine Learning Experiments: Acquire the skills necessary to register models, monitor metrics, and conduct experiments using Azure Machine Learning.
• Optimize Model Training with Hyperparameter Tuning: To enhance model performance, leverage Azure Machine Learning’s features.
• Deploy Models for Batch and Real-time Inferencing: Become proficient with the Azure batch and real-time prediction deployment process.
• Build and Manage End-to-End Machine Learning Pipelines: Acquire the ability to coordinate machine learning processes using pipelines to ensure scalability and reproducibility.
• Interpret and Explain Models for Accountability: Use interpretability and fairness tools to ensure responsible AI and gain insights into model behavior.
• Track Model Performance and Data Drift in Production: To sustain and enhance model dependability over time, track model performance and data drift.