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Azure Data Scientist Training (DP-100)

The Azure Data Scientist Training (DP-100) course aligns with the Microsoft Certified: Azure Data Scientist Associate role and focuses on applying data science and machine learning expertise using Azure Machine Learning. This Azure Data Scientist Training is ideal for professionals with existing Python and machine learning experience who want to transition their skills to the Azure cloud. The course introduces best practices for model monitoring, versioning, and responsible AI, helping learners design solutions that are not only accurate but also reliable and maintainable in enterprise environments.

The Azure Data Scientist Training (DP-100) program IS designed to help data professionals confidently build, deploy, and operationalize machine learning solutions in the cloud. By completing this training, participants will be able to:

Design and configure Azure Machine Learning workspaces for data science workloads
Train, evaluate, and optimize machine learning models using Python and MLflow
Deploy and operationalize models for real-time and batch inference on Azure
Monitor, manage, and retrain machine learning models in production
Prepare confidently for the DP-100 exam and Azure Data Scientist Associate role

Advance Your Skills with Flexmind (Microsoft Partner)

Who should attend the DP-100: Designing and Implementing a Data Science Solution on Azure course ?

For Professionals

This course is designed for professionals who already have data science and machine learning knowledge and want to apply these skills to build and operate ML solutions on Azure. Appropriate job roles: Data Scientist, Machine Learning Engineer, Data Engineer (ML‑focused), Advanced Data Analyst,

For Businesses

Organizations should nominate employees involved in building, deploying, or managing machine learning and AI solutions, especially those transitioning from experimentation to production‑grade ML systems on Azure. Recommended roles to nominate Data Science Teams, AI/ML Engineering Teams, Advanced Analytics Teams.

Prerequisites for the "DP-100: Designing and Implementing a Data Science Solution on Azure" Course

Before attending this course, students should have:

Data Science Experience: Proficiency in using Python to explore, visualize, and prepare data.
Machine Learning Skills: Experience in training, validating, and optimizing models using frameworks such as Scikit-Learn, PyTorch, and TensorFlow.
Azure Knowledge: Ability to create and manage resources within the Microsoft Azure portal.
Technical Proficiency: Understanding of containerization and basic REST API concepts.

Key Features of Flexmind DP-100: Designing and Implementing a Data Science Solution on Azure Training

This training is delivered by Flexmind through flexible online and offline formats and is designed to align with the most current certification exam requirements. The key features of this training are as follows:

4 Day · 32 Hours

Microsoft Certified Trainer

Microsoft Official Curriculum

Cloud Lab Access

Applied Workshop

Course Duration

The course has a total duration of 32 hours and is completed over 4 days.

Instructor-Led Training

Delivered by a senior Microsoft Certified Trainer with real-world, enterprise-scale experience in designing and developing Data Science solutions in Microsoft Azure.

Microsoft Official Courseware

Delivered by Flexmind using official Microsoft courseware, this program blends study material, hands‑on labs, and applied workshops with instructor-led guidance throughout.

Applied Workshop

The final‑day applied workshop allows learners to practice their skills by selecting a scenario and building a complete solution, including any required automation.

Course Completion Certificate

Course completion includes certification, formally validating the skills gained and reinforcing professional credibility.

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

Module 1: Design a machine learning training solution

Understanding the machine learning lifecycle
Identifying ML problem types (classification, regression, NLP, vision)
Sourcing and preparing training data
Designing data storage and access strategies
Building data ingestion pipelines using Azure services
Training and evaluating machine learning models
Selecting appropriate Azure ML training services
Choosing compute options (CPU, GPU, Spark clusters)
Deploying models using endpoints
Designing real-time vs batch prediction solutions
Implementing MLOps lifecycle and workflows
Monitoring model performance and drift
Lab - Exploring Azure Machine Learning workspace

Module 2: Explore and configure the Azure Machine Learning workspace

Understanding Azure Machine Learning fundamentals
Creating and configuring an ML workspace
Exploring workspace and managing access (RBAC)
Managing workspace resources and datastores
Choosing and configuring compute targets
Optimizing compute usage and cost management
Understanding URIs and data access methods
Working with datastores and data assets
Creating and managing ML assets (data, models, components)
Managing environments and dependencies
Creating custom environments using Docker and Conda
Exploring model training approaches (AutoML, notebooks, jobs)
Running notebooks and scripts as jobs
Exploring Azure ML studio and workflows
Using developer tools (Python SDK, Azure CLI, VS Code)
Lab - Exploring Azure ML workspace and tools

Module 3: Experiment with Azure Machine Learning

Exploring model training approaches in Azure ML
Understanding Automated Machine Learning (AutoML)
Selecting ML tasks (classification, regression, NLP, vision)
Preparing data and configuring MLTable assets
Applying scaling, normalization, and feature engineering
Running AutoML experiments using Python SDK
Restricting and selecting ML algorithms
Configuring AutoML experiments and training limits
Evaluating and comparing trained models
Reviewing preprocessing and data guardrails
Identifying best model and experiment runs
Tracking ML experiments using MLflow
Configuring MLflow for notebooks and scripts
Logging parameters, metrics, and artifacts
Comparing runs and visualizing metrics in Azure ML
Lab - Building and tracking models using AutoML and MLflow

Module 4: Optimize model training with Azure Machine Learning

Running training scripts as command jobs
Converting notebooks into production-ready scripts
Configuring and executing command jobs
Using parameters to customize training jobs
Tracking training jobs with MLflow
Logging metrics, parameters, and artifacts
Evaluating experiment results in Azure ML studio
Performing hyperparameter tuning with sweep jobs
Defining hyperparameter search spaces
Configuring sampling strategies (grid, random, Bayesian)
Applying early termination policies for optimization
Creating reusable components for ML workflows
Building pipelines using components
Running and monitoring pipeline jobs
Scheduling pipelines for automated retraining
Lab - Running scripts, tuning models, and building pipelines

Module 5: Manage and evaluate models in Azure Machine Learning

Registering models using MLflow in Azure ML
Logging models as artifacts and deployable assets
Understanding MLmodel format and metadata
Preparing models for deployment lifecycle
Understanding Responsible AI principles
Analyzing dataset distribution and data quality
Evaluating model errors and performance gaps
Interpreting models using feature importance
Assessing fairness and mitigating bias
Creating Responsible AI dashboards
Using RAI components for model insights
Lab - Building and exploring Responsible AI dashboards

Module 6: Deploy and consume models with Azure Machine Learning

Deploying models to managed online endpoints
Understanding real-time inference endpoints
Deploying MLflow and custom models
Testing and consuming real-time endpoints
Implementing batch inference with batch endpoints
Creating and configuring batch endpoints
Deploying models for batch scoring jobs
Using compute clusters for batch processing
Configuring batch inference settings and outputs
Deploying custom models with scoring scripts
Invoking batch endpoints with data inputs
Monitoring and managing batch inference jobs
Troubleshooting batch scoring pipelines and logs
Lab - Deploying and consuming models using endpoints

Module 7: Optimize language models for generative AI applications

Understanding Azure AI Foundry fundamentals
Setting up hubs, projects, and connections
Building AI apps with models and workflows
Deploying models with performance configurations
Managing collaboration and access control

Class Schedule

Instructor‑Led Training

32 Hours of Instructor‑Led Training
One‑to‑one doubt‑resolution sessions
Microsoft Official Lab Access

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

After completing the DP-100 course, learners will be able to:

Design and implement end-to-end machine learning solutions using Azure Machine Learning services
Prepare, manage, and process data effectively for model training and experimentation
Build, train, and optimize machine learning models using AutoML, SDK, and pipelines
Deploy and operationalize models for real-time and batch inference with MLOps practices
Monitor, evaluate, and refine models to ensure performance, reliability, and responsible AI compliance

About DP-100 Certification Exam

To help you understand the assessment better, here are a few important details about the exam.

Exam Name	DP-100: Designing and Implementing a Data Science Solution on Azure
Who should Apply	Data Scientist
Duration of Exam	100 Minutes
Fees	Rs. 4,865 (India), $165 USD (United States)
Level of Difficulty	Intermediate
Type of Credential	Microsoft Certification
Languages	English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia)
Exam Retake	Exam retake allowed after 24 hours
Quality Check during Assessment	The online exam is proctored

The table below represents the weightage of each study area in the exam. Areas with higher percentages are expected to have more questions.

Study Area	Percentage
Design and prepare a machine learning solution	20-25%
Explore data, and run experiments	20-25%
Manage risks, alerts, and activities	30-35%
Train and deploy models	25-30%
Optimize language models for AI applications	25-30%

Reviews

Dipak Agarwal - Business Analyst at Genpact

(5/5)

Excellent professional & comfortable environment to learn

The training was good, I would like to go through some more training.

Gurpreet Marwaha - IAM Analyst at National Australia Bank, Australia

(5/5)

I learned a lot

Excellent Office 365 training provided by Flexmind

Kiranmai Yachareni - Web Developer at Total Quality Logistics, USA

(5/5)

Excellent ability to clarify all the queries I raised

Thank You! I would definitely recommend it to others. The training session was very informative and interactive. Very useful and definitely thought-provoking. All the module sessions were clear and interactive, great, and friendly. Catered to all levels, which was good for those just starting out and those with more experience. I really appreciate the recorded videos and training material, which would be definitely helpful for later references.

Praveen S (Associate at Grant Thornton LLP)

(5/5)

Excellent knowledge of the trainer on the Subject

I attended PowerApps/Flow training from Flexmind. The topics of the course were well organized & knowledge of the trainer on the subject was excellent. It was very helpful and I will apply in my day to day task at the office. I will definitely recommend a friend or colleague to attend courses from Flexmind.

FAQ's About DP-100: Designing and Implementing a Data Science Solution on Azure Course

This course equips you with the skills to design, build, deploy, and manage end‑to‑end machine learning solutions using Azure Machine Learning. It focuses on practical implementation of data science workflows, including data preparation, model training, deployment, and monitoring in a cloud environment.

This course is ideal for data scientists, AI/ML engineers, and developers who want to build and operationalize machine learning solutions on Azure. It is also suitable for professionals with prior experience in Python and machine learning who want to validate their skills with the Azure Data Scientist Associate certification.

Participants should have a working knowledge of Python programming and basic machine learning concepts such as classification, regression, and model evaluation. Familiarity with data processing tools and cloud fundamentals will help maximize learning outcomes.

The course covers designing machine learning solutions, managing Azure Machine Learning workspaces, preparing and exploring data, building and training models, optimizing models, deploying models (real-time and batch), and implementing MLOps and Responsible AI practices.

Yes, the training is highly hands-on. Participants will work with Azure Machine Learning, use SDKs and tools like AutoML, and build real-world machine learning workflows, including deployment and monitoring scenarios.

Yes, the course is fully aligned with the DP‑100 certification objectives and prepares you for the Microsoft Certified: Azure Data Scientist Associate exam, covering all key domains such as data preparation, model training, deployment, and optimization.

This course is delivered by a Microsoft Certified Trainer (MCT) with extensive real-world experience in Azure Machine Learning, data science workflows, and enterprise AI implementations.

You will gain hands-on experience with Azure Machine Learning, Python SDK, AutoML, MLflow, pipelines, model deployment (endpoints), and model monitoring techniques used in enterprise environments.

Unlike generic data science courses, DP‑100 focuses on implementing data science solutions specifically on Azure, emphasizing cloud-based machine learning lifecycle management, scalability, and production deployment practices.

By the end of the course, you will be able to design and implement machine learning solutions, train and deploy models at scale, automate workflows, and manage the complete ML lifecycle in Azure with best practices for performance and governance.

The course is delivered over 4 days as an instructor-led training (ILT), combining theory, demos, and hands-on labs guided by an MCT to ensure practical learning.

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