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

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Find out more about DP-100: Designing and Implementing a Data Science Solution on Azure

The DP-100: Designing and Implementing a Data Science Solution on Azure course prepares learners to build, deploy, and manage machine learning solutions using Azure Machine Learning. It covers data preparation, experimentation, model training, and deployment in cloud environments. Participants gain hands-on skills in automating workflows, optimizing performance, and ensuring scalability and security. The course emphasizes using Azure tools and services for real-world AI solutions, aligning with the DP-100 certification exam for aspiring Azure Data Scientists. 

Duration
35 hours
Fees
$399.00
Goal
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About DP-100: Designing and Implementing a Data Science Solution on Azure

  • Learn to design and implement end-to-end Azure data solutions.

  • Explore Azure Machine Learning for scalable model training deployments.

  • Perform data preparation, transformation, and feature engineering using Azure.

  • Build, train, and evaluate machine learning models effectively on Azure.

  • Automate machine learning workflows with pipelines, experiments, and version control.

  • Deploy predictive models as secure, scalable, and managed services.

  • Monitor, retrain, and optimize models for consistent production performance.

  • Apply responsible AI practices ensuring fairness, security, and compliance.

  • Gain hands-on labs to master Azure data science solutions.

  • Prepare thoroughly for Microsoft DP-100 certification and career advancement.

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

BEST SELLER

DP-100 Training

Instructor-led live online or offline classroom training
Comprehensive coverage of all exam objectives
Expert-led video lessons and guided tutorials
Practice exams with detailed answer explanations
Downloadable study guides and cheat sheets
Real-world scenarios to build practical skills
Interactive quizzes to reinforce learning outcomes
Enroll Now →
BEST SELLER

DP-100 Corporate Package

Instructor-led live online or offline classroom training
Comprehensive coverage of all exam objectives
Expert-led video lessons and guided tutorials
Practice exams with detailed answer explanations
Downloadable study guides and cheat sheets
Real-world scenarios to build practical skills
Interactive quizzes to reinforce learning outcomes
Enroll Now

Course Contents

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  • Course objectives, exam alignment (DP-100), and success tips
  • Azure Machine Learning workspace concepts and architecture
  • Setup: Azure subscription, resource group, role assignments
  • Lab: Create and configure an Azure ML workspace
  • Data ingestion from Azure Blob, ADLS, and SQL sources
  • Data cleaning, missing values, and exploratory data analysis
  • Feature engineering, encoding, scaling, and pipelines
  • Lab: EDA and feature engineering in Jupyter with Azure ML
  • Designing experiments and using Azure ML experiments SDK
  • Local vs. cloud compute targets (compute instances, clusters)
  • Hyperparameter tuning and automated ML (AutoML) basics
  • Lab: Train models on compute cluster and run hyperparameter sweeps
  • Model registry, tracking runs, artifacts, and metadata
  • Model packaging and reproducibility (environments, conda, Docker)
  • Model lineage and model comparison best practices
  • Lab: Register and version models in Azure ML
  • Fairness, bias detection, privacy, and explainability techniques
  • Using interpretability tools (SHAP, feature importance, causal checks)
  • Governance, compliance considerations, and documentation practices
  • Lab: Evaluate model fairness and generate explainability reports
  • Real-time vs. batch inference patterns and architecture decisions
  • Deploying to Azure Container Instances, AKS, and Azure Functions
  • Endpoint management, authentication, and scaling strategies
  • Lab: Deploy a model as a REST endpoint and test predictions
  • Model and data drift detection, telemetry, and logging
  • CI/CD pipelines for ML: Git integration, pipelines, and triggers
  • Retraining strategies, blue/green and canary deployments
  • Lab: Build an Azure DevOps/GitHub Actions pipeline for model deployment
  • Building reusable ML pipelines with Azure ML Pipelines SDK
  • Orchestrating multi-step workflows and caching for efficiency
  • Integration with Databricks, Data Factory, and event-based triggers
  • Lab: Create and run a multi-step training + deployment pipeline
  • Profiling models for latency and throughput improvements
  • Using ONNX, model quantization, and hardware accelerators (GPU/FPGA)
  • Cost governance: spot VMs, autoscaling, and lifecycle policies
  • Lab: Optimize model performance and estimate deployment costs
  • End-to-end project: ingest data, train, deploy, monitor a production model
  • Best-practices checklist, common exam topics, and practice questions
  • Course wrap-up, resources, and next steps for certification
  • Lab: Deliver capstone solution and present results

Eligibility of the course

  • Basic knowledge of Python programming for data science and machine learning.
  • Understanding of fundamental statistics and machine learning concepts (regression, classification, clustering).
  • Familiarity with Azure fundamentals (subscription, resources, and cloud basics).
  • Experience with data preparation and analysis using tools like pandas, NumPy, or similar.
  • Comfort with working on Jupyter notebooks or equivalent environments.
  • Optional but helpful: prior experience with Azure Machine Learning or other ML platforms.

Key takeaways

  • Comprehensive Study material
  • Access to Practice tests
  • Access to LMS course content
  • Access to course assignments
  • Industry-relevant case-studies


Course features

Instructor-led live online or offline classroom training

Comprehensive coverage of all exam objectives

Expert-led video lessons and guided tutorials

Practice exams with detailed answer explanations

Downloadable study guides and cheat sheets

Real-world scenarios to build practical skills

Interactive quizzes to reinforce learning outcomes

Course Benefits

 Validates Azure Data Science skills globally. 

 Practical labs with real-world machine learning projects. 

 Gain proficiency in Azure ML workspace, pipelines, and services. 

 From data prep to deployment and monitoring. 

 Prepares for roles like Azure Data Scientist and ML Engineer. 

 Learn fairness, bias detection, and interpretability practices. 

 Deploy models efficiently with cloud compute and cost management. 

 Aligns directly with Microsoft DP-100 certification objectives. 

 Work with Databricks, Data Factory, CI/CD pipelines, and APIs. 

 Use Azure automation and pipelines for faster ML lifecycle. 

FAQs

Asked Questions & Answer

 The DP-100 course teaches how to design, build, deploy, and manage machine learning solutions using Azure Machine Learning services. 

 It is ideal for aspiring data scientists, AI engineers, and professionals wanting to implement ML solutions on Microsoft Azure. 

 Yes. Basic Python knowledge, machine learning fundamentals, and familiarity with Azure cloud concepts are recommended. 

 You’ll learn data preparation, model training, experimentation, deployment, monitoring, and responsible AI practices using Azure ML. 

 Yes. It aligns with the Microsoft DP-100 certification exam for Azure Data Scientist Associate. 

 It is offered through instructor-led training .

 DP-100 certification validates your Azure Data Science expertise, enhancing job opportunities in AI, ML, and cloud-based data science roles. 

 Yes. Microsoft’s DP-100 certification is globally recognized and valued by organizations hiring data science and AI professionals. 

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