Microsoft Certified Azure Data Scientist Associate (DP-100)

Overview

This training is designed for individuals who want to apply their data science and machine learning skills on the Azure platform. It covers topics such as data exploration, model training, and deployment using Azure Machine Learning. Participants will learn to use Azure services to build, train, and deploy machine learning models, and to optimize and manage these models in a production environment.

Course scope

Azure Data Scientist – Associate training is designed for individuals who want to apply their data science and machine learning skills on the Azure platform. Participants will learn to explore and prepare data for analysis using Azure tools, including data cleaning, transformation, and feature engineering techniques. The training covers model training and evaluation, teaching how to train machine learning models using Azure Machine Learning and evaluate their performance to select the best models for deployment

What you'll learn

Design and Prepare a Machine Learning Solution
Explore Data and Train Models
Prepare a Model for Deployment
Deploy and Retrain a Model
Implement Responsible Machine Learning
Prepare for the Certification Exam

Certification detail — Microsoft (DP-100)

The DP-100 certification demonstrates your ability to leverage Azure’s machine learning services to build, train, and deploy machine learning models. It covers key aspects of data science workflows, including data preparation, model training, evaluation, and deployment. The Microsoft Certified Azure Data Scientist Associate (DP-100) certification is designed for professionals who want to validate their skills in designing and implementing data science solutions on Microsoft Azure. Here’s an overview of the certification, including its scope, career impact, and relevant job roles:

Key Objectives of DP-100 Certification:

Data Science and Machine Learning: Proficiency in using Azure Machine Learning to build, train, and deploy machine learning models.

Data Preparation: Skills in preparing data for analysis and modeling using Azure’s data services.

Model Training: Understanding of various machine learning algorithms and techniques for training and tuning models.

Model Deployment: Ability to deploy models into production environments and manage their lifecycle.

Plan and Create Azure Machine Learning Workspaces:

Workspace Setup:

Create Azure Machine Learning Workspace: Set up and configure an Azure Machine Learning workspace, which includes creating resource groups, configuring compute instances, and setting up environments.

Manage Workspace Resources: Handle various resources within the workspace such as compute clusters, storage accounts, and datasets.

Experimentation and Tracking:

Experimentation: Configure and manage experiments within the workspace, tracking runs, and metrics to monitor the progress and performance of model training.

Data Management: Organize and manage datasets, dataset versions, and data sources to ensure smooth data flow and accessibility.

Prepare Data for Machine Learning:

Data Ingestion and Integration:

Data Ingestion: Use Azure services (such as Azure Blob Storage, Azure Data Factory) to ingest data from various sources.

Data Integration: Combine and integrate data from different sources to prepare it for analysis and modeling.

Data Cleaning and Transformation:

Data Preparation: Clean and preprocess data to address issues like missing values, outliers, and inconsistencies using Azure Data Factory, Azure Databricks, or Azure Synapse Analytics.

Feature Engineering: Transform raw data into meaningful features that enhance model performance.

Perform Data Analysis:

Exploratory Data Analysis (EDA):

Data Exploration: Analyze data distributions, correlations, and patterns using Azure Machine Learning, Power BI, or other visualization tools.

Statistical Analysis: Apply statistical methods to summarize data and understand relationships between variables.

Visualization and Interpretation:

Data Visualization: Use tools like Matplotlib, Seaborn, and Azure Machine Learning’s visualization capabilities to create plots and graphs that represent data insights.

Insight Generation: Generate actionable insights from data analysis to inform model development and business decisions.

Train Models:

Algorithm Selection:

Choose Algorithms: Select appropriate machine learning algorithms based on the problem type (e.g., classification, regression, clustering) and data characteristics.

Algorithm Implementation: Implement algorithms using Azure Machine Learning’s AutoML or custom code to train models.

Model Training and Evaluation:

Model Training: Train models using Azure Machine Learning, ensuring that the training process is efficient and effective.

Hyperparameter Tuning: Optimize model performance by tuning hyperparameters and evaluating different configurations.

Model Deployment:

Deploy Models: Deploy trained models to production environments using Azure services such as Azure Kubernetes Service (AKS) or Azure Container Instances (ACI).

Deployment Strategies: Implement deployment strategies like A/B testing and canary releases to ensure model stability and performance.

Model Management and Integration:

Monitor Models: Monitor model performance and behavior in production, managing issues like drift and degradation.

Integration: Integrate deployed models with applications or data pipelines for real-time or batch inference.

Model Updating:

Version Management: Manage model versions and updates, retraining models as necessary to improve performance or adapt to new data.

The DP-100 certification is designed to equip you with the skills needed to effectively use Azure’s machine learning services throughout the entire data science lifecycle. By focusing on planning and creating machine learning workspaces, preparing and analyzing data, training models, and deploying and managing those models, you’ll be prepared to handle complex data science tasks and deliver impactful machine learning solutions on Azure.

Exam Domains:

The DP-100 exam is divided into several domains, each focusing on different aspects of data science and machine learning on Azure. Here’s a breakdown of these domains:

Plan and Create Azure Machine Learning Workspaces (15-20%):
- Workspace Creation: Setting up and configuring Azure Machine Learning workspaces.
- Environment Management: Creating and managing compute resources, environments, and compute instances.
- Experimentation Setup: Establishing experiments and tracking metrics.

Prepare Data for Machine Learning (25-30%):
- Data Ingestion: Using Azure data services like Azure Blob Storage and Azure SQL Database to ingest and prepare data.
- Data Cleaning and Transformation: Utilizing Azure Data Factory, Databricks, or other tools to clean, transform, and prepare data for analysis.
- Feature Engineering: Selecting and engineering features to enhance model performance.

Perform Data Analysis (25-30%):
- Exploratory Data Analysis (EDA): Analyzing data distributions, correlations, and patterns using Azure Machine Learning and other tools.
- Statistical Analysis: Applying statistical methods to understand data and inform model choices.
- Data Visualization: Using tools like Azure Machine Learning, Power BI, and Matplotlib to visualize data and insights.

Train Models (25-30%):
- Algorithm Selection: Choosing appropriate machine learning algorithms based on problem requirements.
- Model Training: Implementing and training models using Azure Machine Learning’s automated ML or custom training scripts.
- Hyperparameter Tuning: Fine-tuning model parameters to optimize performance.

Deploy and Consume Models (20-25%):
- Model Deployment: Deploying trained models to Azure services such as Azure Kubernetes Service (AKS) or Azure Container Instances.
- Model Management: Managing model versions, monitoring performance, and updating models as needed.
- Integration: Integrating models with applications and data pipelines for real-time predictions.