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

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About Course

The DP-100T01: Designing and Implementing a Data Science Solution on Azure course provides an in-depth exploration of Azure’s machine learning capabilities. It covers the entire data science process from data preparation, model training, model deployment, and model management. Learners will gain practical experience with Azure Machine Learning Service and Azure Machine Learning Studio, learning how to create, train, optimize, and deploy machine learning models at scale.Throughout the course, participants will engage in hands-on labs, such as creating an Azure Machine Learning workspace, running experiments, working with datastores and datasets, and orchestrating machine learning workflows with pipelines. They will also explore real-time and batch inferencing, ensuring their models can respond promptly or handle large-scale processing.By mastering hyperparameter tuningautomated machine learning, and model interpretation, students will be well-equipped to build responsible AI solutions. They’ll also delve into the best practices for monitoring models to maintain optimal performance over time, using tools like Application Insights and data drift monitoring. This course is ideal for aspiring and existing data scientists looking to harness the power of Azure to streamline and enhance their machine learning workflows.

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What Will You Learn?

  • Course Prerequisites
  • 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
  • Utilize Azure Machine Learning Tools
  • Run Automated Machine Learning Experiments
  • Build and Publish Models Using Designer
  • Execute and Track Machine Learning Experiments
  • Optimize Model Training with Hyperparameter Tuning
  • Deploy Models for Real-time and Batch Inferencing
  • Create and Manage End-to-End Machine Learning Pipelines
  • Interpret and Explain Models for Accountability
  • Monitor Models and Data Drift in Production