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Designing & Implementing a Data Science Solution on Azure (DP-100T01) Certification
DP-100 Certification Training
The 4-day instructor-led online DP-100T01: Designing and Implementing a Data Science Solution on Azure training in the USA gives professionals the ability to construct, deploy and supervise machine learning models on Microsoft Azure. The extensive program teaches candidates to pass the Microsoft
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DP-100T01: Designing & Implementing a Data Science Solution on Azure Course Overview
The DP-100T01: Designing and Implementing a Data Science Solution on Azure course in the USA provides training for data scientists and AI professionals who wish to construct and enhance machine learning solutions using Microsoft Azure. Students who finish this practical program will achieve Microsoft Certified: Azure Data Scientist Associate certification which demonstrates their capabilities to create, train and launch machine learning models through Azure Machine Learning. This program delivers instruction on the complete data science process beginning with data cleansing and continuing through feature creation and model selection before concluding with model deployment solutions for cloud systems.
The participants will learn hands-on Azure Machine Learning techniques through which they will understand model training processes and develop automated machine learning workflows. The training demonstrates how to deploy models for production environments while teaching methods to monitor their performance for scalability and reliability. The program provides knowledge on how to integrate ML models with Azure services while implementing Responsible AI principles and utilizing AutoML capabilities from Azure. The training provides essential knowledge to run AI-based business solutions by teaching students how to configure data storage systems and prepare datasets and validate models.
Learners learn everything about managing computational resources and adjusting hyperparameters while optimizing model performance. The training focuses on security needs along with compliance standards and economical approaches to implement machine learning solutions on Azure infrastructure. The combination of expert training and practical case studies and hands-on lab sessions helps participants build their technical competence in designing AI solutions which fulfil enterprise needs.
The completion of this course prepares professionals to obtain the Azure Data Scientist Associate certification which showcases their ability to deploy and sustain scalable AI and machine learning solutions on Microsoft Azure.
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Course Objectives
- Understand the end-to-end data science workflow on Microsoft Azure, including data ingestion, pre-processing, model training, and deployment.
- Learn to configure and manage Azure Machine Learning workspaces, compute resources, and environments for efficient AI model development.
- Gain hands-on experience in building, training, and optimizing machine learning models using Azure Machine Learning Studio and SDK.
- Implement feature engineering, hyperparameter tuning, and model validation techniques to improve model accuracy and performance.
- Automate machine learning workflows using Azure ML pipelines and AutoML to streamline model development and deployment.
- Deploy machine learning models as web services, integrate them with Azure Synapse Analytics and Azure Cognitive Services, and monitor their performance.
- Understand Responsible AI principles, including fairness, transparency, and security, to ensure ethical AI model implementation.
- Apply governance, compliance, and security best practices to protect machine learning models and data in Azure environments
- Utilize logging, debugging, and performance monitoring tools to maintain and optimize deployed AI models over time.
- Prepare for the Microsoft Certified: Azure Data Scientist Associate certification by mastering real-world applications of machine learning on Azure.
Audience
- Technical Consultants
- AI/ML Researchers
- Enterprise Architects
- Data Scientists
- Machine Learning Engineers
- AI Professionals
- Data Analysts
- Cloud Engineers
- IT Professionals
- Software Developers
- Business Intelligence Analysts
- DevOps Engineers
- IT Managers
- Cloud Architects
- System Administrators
Eligibility Criteria
Required:
- Working with containers
- Creating cloud resources in Microsoft Azure.
- Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
Recommended:
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
- AI-900T00: Microsoft Azure AI Fundamentals
Course Outline
Designing a Machine Learning Solution
Planning Data Ingestion for Machine Learning
- Identify suitable data sources and formats
- Select an appropriate method for serving data to ML workflows
- Develop a structured data ingestion pipeline
Structuring Machine Learning Model Training
- Define strategies for acquiring and preparing data
- Choose the right service and compute resources for model training
- Plan for deployment by selecting suitable model preparation techniques
Deploying Machine Learning Models
- Analyze model consumption requirements
- Determine deployment strategies: real-time or batch endpoints
Implementing Machine Learning Operations (MLOps)
- Understand the MLOps architecture and workflow
- Design effective monitoring strategies for deployed models
- Establish retraining mechanisms to maintain model performance
Exploring and Configuring the Azure Machine Learning Workspace
Understanding Azure Machine Learning Workspace
- Set up an Azure Machine Learning workspace
- Identify key resources and assets within the workspace
- Train models using the workspace environment
Developer Tools for Workspace Interaction
- Navigate the Azure Machine Learning Studio
- Utilize the Python Software Development Kit (SDK)
- Manage workflows using the Azure Command Line Interface (CLI)
Managing Data in Azure Machine Learning
- Access data via Uniform Resource Identifiers (URIs)
- Connect to cloud data sources using datastores
- Leverage data assets for structured file and folder access
Configuring Compute Targets in Azure Machine Learning
- Select appropriate compute resources for model training
- Work with compute instances and clusters
- Manage dependencies and installed packages using environments
Working with Environments in Azure Machine Learning
- Understand the role of environments in Azure Machine Learning
- Explore and utilize pre-configured (curated) environments
- Create and customize environments for specific use cases
Experimenting with Azure Machine Learning
Automating Classification Model Selection with AutoML
- Prepare data for Automated Machine Learning (AutoML) classification
- Configure and execute an AutoML experiment
- Evaluate and compare generated models
Tracking Model Training with MLflow in Jupyter Notebooks
- Set up MLflow for tracking in Jupyter notebooks
- Use MLflow to monitor and manage model training experiments
Optimizing Model Training with Azure Machine Learning
Executing Training Scripts as Command Jobs
- Convert Jupyter notebooks into standalone scripts
- Test scripts in a terminal environment
- Run scripts as command jobs in Azure Machine Learning
- Utilize parameters to customize command job execution
Tracking Model Training with MLflow
- Integrate MLflow for tracking script-based jobs
- Analyze metrics, parameters, artifacts, and model outputs from training runs
Hyperparameter Tuning in Azure Machine Learning
- Define a structured hyperparameter search space
- Configure sampling strategies for hyperparameter tuning
- Implement early-termination policies for efficient training
- Execute hyperparameter optimization with sweep jobs
Running Pipelines in Azure Machine Learning
- Develop reusable components for machine learning workflows
- Construct and organize Azure Machine Learning pipelines
- Execute and manage ML pipelines for streamlined automation
Managing and Reviewing Models in Azure Machine Learning
Registering MLflow Models in Azure Machine Learning
- Log machine learning models using MLflow
- Understand the MLmodel format and its components
- Register MLflow models within Azure Machine Learning for tracking and deployment
Implementing Responsible AI in Azure Machine Learning
- Explore built-in Responsible AI components in Azure Machine Learning
- Create a Responsible AI dashboard for model assessment
- Analyze and interpret model insights using the Responsible AI dashboard
Deploying and Consuming Models with Azure Machine Learning
Deploying Models to Managed Online Endpoints
- Utilize managed online endpoints for real-time model serving
- Deploy MLflow models to managed online endpoints
- Deploy custom models to managed online endpoints
- Test and validate deployed online endpoints
Deploying Models to Batch Endpoints
- Create batch endpoints for large-scale model inference
- Deploy MLflow models to batch endpoints
- Deploy custom models to batch endpoints
- Invoke batch endpoints for processing multiple predictions
About The Certification
Microsoft Certified Azure Data Scientist Associate:
This credential validates a professional’s expertise in data science and their ability to execute machine learning workloads on Azure. Individuals involved in developing machine learning models can pursue this certification to demonstrate their proficiency.
Who Should Pursue This Certification?
Candidates should have expertise in leveraging data science and machine learning techniques to build and manage machine learning solutions on Azure.
Key Responsibilities:
Designing and setting up an optimal environment for data science projects
Analyzing and pre-processing data
Training machine learning models
Building and managing ML pipelines
Executing jobs to facilitate production readiness
Deploying, scaling, and monitoring machine learning solutions
Technologies Covered:
- Azure Machine Learning
- MLflow
Exam Domains & Weightage:
- Design and set up a machine learning solution (20–25%)
- Analyze data and train models (35–40%)
- Prepare models for deployment (20–25%)
- Deploy and update models (10–15%)
Exam Details:
- Passing Score: 700 or above
- Duration: 120 minutes
Recertification Policy:
- Previously, Microsoft role-based and specialty certifications were valid for two years.
- Since June 2021, certifications remain valid for one year. However, candidates can renew them for free via Microsoft Learn.
- The renewal window starts six months before expiration, allowing candidates to take an online assessment to extend the certification by an additional year.
- Certifications obtained before June 2021 remain valid for two years and are eligible for the new renewal process.
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- Domain-customization
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- Instructor-Led Skill Development Program
FAQ’s
What is the Microsoft Certified: Azure Data Scientist Associate certification in the USA?
This certification demonstrates proficiency in leveraging data science and machine learning methodologies to develop and manage ML workloads on Microsoft Azure. It is tailored for professionals specializing in cloud-based AI solutions.
What are the prerequisites for this certification?
Although no formal prerequisites exist, candidates should have prior experience with data science principles, Python programming, and machine learning fundamentals. Understanding Azure Machine Learning and MLflow is beneficial.
Which exam is required to obtain this certification?
Candidates must pass Exam DP-100: Designing and Implementing a Data Science Solution on Azure, which assesses expertise in data pre-processing, model development, MLOps strategies, and ML model deployment on Azure.
How long does the certification remain valid?
The certification remains valid for one year and can be renewed at no cost by completing an online assessment within six months before expiration.
What is the passing score for the DP-100 exam in the USA?
A minimum score of 700 out of 1000 is required to pass. Since the exam follows a scaled scoring system, the number of correct answers needed may vary.
What topics are covered in the Azure Data Scientist Associate course?
The course includes essential areas such as:
- Setting up a machine learning environment
- Data exploration and feature selection
- Model training, evaluation, and hyperparameter tuning
- Implementing MLOps pipelines
- Deploying and managing ML models in Azure
Who should enroll in this course?
This course is best suited for data scientists, AI engineers, and ML specialists involved in designing and deploying AI/ML solutions in Azure. It is particularly valuable for professionals in cloud computing, AI development, and data analytics.
Does the course provide hands-on practice?
Yes, the course includes interactive labs and real-world case studies to offer hands-on exposure in model building, training, deployment, and maintenance using Azure services.
What is the duration of the Microsoft Certified: Azure Data Scientist Associate course in the USA?
The course duration typically lasts four days, covering instructor-led sessions, hands-on labs, and exam-oriented practice exercises.
Will this course help in passing the DP-100 exam?
Yes, the course is structured around DP-100 exam objectives, offering in-depth coverage of relevant topics. However, self-study and additional practice tests are recommended for thorough exam preparation.
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Reviews
In-depth and practical! This training seamlessly blends foundational concepts with hands-on applications, making it ideal for data professionals. Expert-led sessions simplify advanced topics like model development, MLOps integration, and cloud-based implementations. Practical exercises offer real-world exposure, while insights into Azure Machine Learning and MLflow significantly enhance learning. Engaging Q&A discussions clarify key concepts, boosting confidence in managing machine learning projects on Azure.
Karthik KannanProject Manager
Highly valuable for AI specialists and data scientists! From data preprocessing to model deployment, this course emphasizes practical application with industry-relevant examples. Step-by-step guidance, detailed walkthroughs, and troubleshooting techniques improve understanding. The focus on hyperparameter optimization, automated machine learning, and ethical AI practices makes this training especially relevant, enhancing both technical skills and career opportunities.
Vivekanand MunisamySecurity Administrator
A transformative learning experience for ML professionals! With a structured curriculum, expert insights, and practical case studies, even complex subjects become accessible. Sessions on feature engineering, performance monitoring, and MLOps workflows provide immense value. Completing this course has expanded my skill set, equipping me for advanced roles in AI and cloud-based machine learning.
Santanu MaityData Engineer
Exceptional Azure ML training for all skill levels! Whether you're new to the field or an experienced professional, this course thoroughly covers model experimentation, tuning, and deployment. Interactive labs and real-world simulations make learning immersive. The deep dive into Azure Machine Learning, AI governance, and pipeline automation strengthens problem-solving abilities and optimization strategies for scalable AI solutions.
Rishi SrivastavaData Scientist
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