Microsoft Azure Data Scientist Associate (DP-100)

Microsoft Certified: Azure Data Scientist Associate (DP-100)

Module 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

Getting Started with Azure Machine Learning

Azure Machine Learning Tools

Lab : Creating an Azure Machine Learning Workspace

Lab : Working with Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons

Training Models with Designer

Publishing Models with Designer

Lab : Creating a Training Pipeline with the Azure ML Designer

Lab : Deploying a Service with the Azure ML Designer

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

Introduction to Experiments

Training and Registering Models

Lab : Running Experiments

Lab : Training and Registering Models

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

Working with Datastores

Working with Datasets

Lab : Working with Datastores

Lab : Working with Datasets

Module 5: Compute Contexts

One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

Working with Environments

Working with Compute Targets

Lab : Working with Environments

Lab : Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Lessons

Introduction to Pipelines

Publishing and Running Pipelines

Lab : Creating a Pipeline

Lab : Publishing a Pipeline

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

Real-time Inferencing

Batch Inferencing

Lab : Creating a Real-time Inferencing Service

Lab : Creating a Batch Inferencing Service

Module 8: Training Optimal Models

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

Hyperparameter Tuning

Automated Machine Learning

Lab : Tuning Hyperparameters

Lab : Using Automated Machine Learning

Module 9: Interpreting Models

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

Lessons

Introduction to Model Interpretation

using Model Explainers

Lab : Reviewing Automated Machine Learning Explanations

Lab : Interpreting Models

Module 10: Monitoring Models

After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

Lessons

Monitoring Models with Application Insights

Monitoring Data Drift

Lab : Monitoring a Model with Application Insights

Lab : Monitoring Data Drift

Microsoft Certified: Azure Data Scientist Associate (DP-100)

Module 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

Getting Started with Azure Machine Learning

Azure Machine Learning Tools

Lab : Creating an Azure Machine Learning Workspace

Lab : Working with Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons

Training Models with Designer

Publishing Models with Designer

Lab : Creating a Training Pipeline with the Azure ML Designer

Lab : Deploying a Service with the Azure ML Designer

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

Introduction to Experiments

Training and Registering Models

Lab : Running Experiments

Lab : Training and Registering Models

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

Working with Datastores

Working with Datasets

Lab : Working with Datastores

Lab : Working with Datasets

Module 5: Compute Contexts

One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

Working with Environments

Working with Compute Targets

Lab : Working with Environments

Lab : Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Lessons

Introduction to Pipelines

Publishing and Running Pipelines

Lab : Creating a Pipeline

Lab : Publishing a Pipeline

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

Real-time Inferencing

Batch Inferencing

Lab : Creating a Real-time Inferencing Service

Lab : Creating a Batch Inferencing Service

Module 8: Training Optimal Models

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

Hyperparameter Tuning

Automated Machine Learning

Lab : Tuning Hyperparameters

Lab : Using Automated Machine Learning

Module 9: Interpreting Models

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model’s behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

Lessons

Introduction to Model Interpretation

using Model Explainers

Lab : Reviewing Automated Machine Learning Explanations

Lab : Interpreting Models

Module 10: Monitoring Models

After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

Lessons

Monitoring Models with Application Insights

Monitoring Data Drift

Lab : Monitoring a Model with Application Insights

Lab : Monitoring Data Drift

• Exam duration: 100 minutes
• Approximately 40–60 multiple-choice, case study and scenario-based questions
• Passing score: 700 out of 1,000
• Closed-book examination – no reference materials are permitted during the exam

• Exam duration: 100 minutes
• Approximately 40–60 multiple-choice, case study and scenario-based questions
• Passing score: 700 out of 1,000
• Closed-book examination – no reference materials are permitted during the exam

• Four days of instructor-led training and exam preparation delivered by an accredited Microsoft trainer
• Official Microsoft course materials, labs and supporting learning resources
• Microsoft certification examination voucher included. Examination vouchers are usually valid for up to 12 months from the date of issue; however, funding rules may require learners funded through schemes such as PLA, ReAct or other government-funded programmes to sit their examination within a shorter timeframe.
• Exam Pass Guarantee – if you do not pass the exam after attending the course, you can retake the same training with NILC at no additional cost. You will only need to pay the examination fee charged by the exam provider. Applies to instructor-led courses only.

• Four days of instructor-led training and exam preparation delivered by an accredited Microsoft trainer
• Official Microsoft course materials, labs and supporting learning resources
• Microsoft certification examination voucher included. Examination vouchers are usually valid for up to 12 months from the date of issue; however, funding rules may require learners funded through schemes such as PLA, ReAct or other government-funded programmes to sit their examination within a shorter timeframe.
• Exam Pass Guarantee – if you do not pass the exam after attending the course, you can retake the same training with NILC at no additional cost. You will only need to pay the examination fee charged by the exam provider. Applies to instructor-led courses only.

Before attending this course, learners should have:

• A fundamental understanding of Microsoft Azure and cloud computing concepts
• Experience writing Python code and working with libraries such as NumPy, Pandas and Matplotlib
• An understanding of machine learning concepts and data science processes
• Experience preparing data and training machine learning models
• Familiarity with machine learning frameworks such as Scikit-Learn, PyTorch or TensorFlow
• Basic knowledge of data analysis, model evaluation and predictive analytics techniques

Before attending this course, learners should have:

• A fundamental understanding of Microsoft Azure and cloud computing concepts
• Experience writing Python code and working with libraries such as NumPy, Pandas and Matplotlib
• An understanding of machine learning concepts and data science processes
• Experience preparing data and training machine learning models
• Familiarity with machine learning frameworks such as Scikit-Learn, PyTorch or TensorFlow
• Basic knowledge of data analysis, model evaluation and predictive analytics techniques