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Designing & Implementing a Data Science Solution on Azure (DP-100T01) Certification Training
The 4-day instructor-led online DP-100T01: Designing and Implementing a Data Science Solution on Azure training in India teaches professionals to create, deploy and optimize machine learning models through Microsoft Azure. The extensive program equips candidates to pass the Microsoft Certified: A
Designing & Implementing a Data Science Solution on Azure (DP-100T01)
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Course Overview
The DP-100T01: Designing and Implementing a Data Science Solution on Azure training program in India provides data science professionals with skills to construct, train and deploy machine learning models through Microsoft Azure. The training program equips participants to pass the Microsoft Certified: Azure Data Scientist Associate certification by teaching Azure Machine Learning service usage and AI workflow automation. The training includes hands-on sessions where students can learn how to handle all stages of the data science pipeline starting with data acquisition and ending with model deployment and monitoring in cloud systems.
The training enables participants to work directly with Azure Machine Learning Studio by learning compute resource configuration as well as model optimization and Responsible AI implementation. The training program includes lessons on data pre-processing as well as feature selection and hyperparameter tuning and model evaluation methods to achieve maximum performance. The course teaches students about integrating machine learning solutions with Azure Synapse Analytics and Azure Cognitive Services to facilitate smooth AI-based decision processes.
Security and governance and compliance represent essential priorities which guarantee that deployed models fulfill industry standards and meet regulatory demands. The training teaches students about Azure’s AutoML and ML pipelines which help automate the development process of AI solutions. The training provides practical expertise in designing data science solutions through expert instruction and hands-on labs and real-world case study application.
Participants who complete this course will have all the necessary skills to pass the Azure Data Scientist Associate certification exam and prove their expertise in building and deploying AI models through Microsoft Azure.
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Course Objectives
- Understand the core concepts of machine learning and data science workflows in the Microsoft Azure environment.
- Learn to set up and manage Azure Machine Learning workspaces, compute clusters, and cloud-based AI environments.
- Develop skills in data pre-processing, feature selection, and model training using Azure Machine Learning Studio and SDK.
- Apply hyperparameter tuning, model evaluation, and performance optimization techniques to improve AI models.
- Automate and orchestrate machine learning workflows using Azure ML pipelines and AutoML.
- Deploy machine learning models as scalable web services and integrate them with Azure Synapse Analytics and Cognitive Services.
- Implement Responsible AI practices, ensuring fairness, transparency, and ethical AI deployment.
- Explore security, governance, and compliance strategies to protect AI models and data in enterprise environments.
- Monitor, troubleshoot, and refine deployed machine learning models for continuous improvement.
- Gain the expertise required to pass the Microsoft Certified: Azure Data Scientist Associate certification exam.
Audience
- IT Managers
- Cloud Engineers
- AI Professionals
- Business Intelligence Analysts
- Data Scientists
- DevOps Engineers
- System Administrators
- Machine Learning Engineers
- AI/ML Researchers
- IT Professionals
- Cloud Architects
- Data Analysts
- Software Developers
- Technical Consultants
- Enterprise Architects
Prerequisite
Required:
- Experience in creating cloud resources within Microsoft Azure.
- Familiarity with training and validating machine learning models using frameworks such as Scikit-Learn, PyTorch, and TensorFlow.
- Understanding of working with containers.
Recommended:
- AI-900T00: Microsoft Azure AI Fundamentals
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
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 & Examination
Certification Overview
Microsoft Azure Data Scientist Associate
This certification confirms an individual's proficiency in data science and their ability to implement machine learning workflows using Azure. Professionals working on machine learning model development can obtain this credential to validate their skills.
Who Should Consider This Certification?
Individuals with experience in applying data science and machine learning methodologies to design and manage ML solutions within Azure.
- Primary Responsibilities:
- Configuring and optimizing environments for data science projects
- Processing and analyzing datasets
- Training machine learning models
- Developing and maintaining ML pipelines
- Running experiments for production readiness
- Deploying, scaling, and monitoring ML models
Technologies Included:
- Azure Machine Learning
- MLflow
- Exam Structure & Weightage:
- Designing and configuring an ML solution (20–25%)
- Data analysis and model training (35–40%)
- Preparing models for deployment (20–25%)
- Deploying and maintaining models (10–15%)
Exam Information:
- Passing Score: 700 or higher
- Duration: 120 minutes
Recertification Guidelines:
- Previously, Microsoft role-based and specialty certifications were valid for two years.
- Since June 2021, certifications remain valid for one year, with free renewal available via Microsoft Learn.
- The renewal process starts six months before expiry, allowing candidates to take an online assessment to extend validity for another year.
- Certifications earned before June 2021 remain valid for two years but can follow the new renewal process.
Choose Your Preferred Mode
Online Training
- Subject Matter-Authorized Experts
- Official Content
- Approved and Quality Ensured training Material
- 24*7 learner assistance and support
Corporate Training
- ROI-optimization & Group Discounts
- Domain-customization
- 24*7 Learner Assistance and Support
- Instructor-Led Skill Development Program
FAQ’s
What does the Microsoft Certified: Azure Data Scientist Associate certification in India represent?
This certification confirms expertise in leveraging data science and machine learning techniques to develop and manage ML workloads on Microsoft Azure. It is intended for professionals specializing in AI and cloud-based machine learning solutions.
Are there any prerequisites for this certification?
No mandatory prerequisites exist, but candidates should have experience with data science fundamentals, Python programming, and machine learning frameworks. Familiarity with Azure Machine Learning and MLflow is highly recommended.
Which exam must be passed to earn this certification?
Candidates need to clear Exam DP-100: Designing and Implementing a Data Science Solution on Azure, which assesses skills in data processing, model development, MLOps integration, and cloud-based model deployment.
What is the validity period of the certification?
The certification remains valid for one year. Candidates can renew it for free by completing an online assessment, available six months before its expiration.
What is the minimum passing score for Exam DP-100 in India?
A score of 700 out of 1000 is required to pass. The exam follows a scaled scoring method, so the number of correct answers needed may differ.
What topics are included in the Azure Data Scientist Associate training?
The course covers:
- Configuring and optimizing machine learning environments
- Data pre-processing and feature engineering
- Model training, evaluation, and hyperparameter tuning
- Implementing MLOps strategies and automation pipelines
- Deploying and managing ML models on Azure
Who should enroll in this training?
This program is best suited for data scientists, AI engineers, and machine learning specialists who design and deploy ML solutions using Azure. It is also valuable for professionals in cloud computing, data analytics, and artificial intelligence fields.
Does this course include hands-on exercises?
Yes, the training features interactive labs and real-world case studies, providing hands-on experience in building, training, deploying, and managing ML models within Azure.
How long does the Azure Data Scientist Associate course last?
The duration generally spans four days, including instructor-led sessions, practical labs, and exam-oriented exercises.
Will this course help me prepare for the DP-100 exam in India?
Yes, the training is aligned with DP-100 exam objectives, offering in-depth coverage of relevant topics. However, additional self-study and practice tests are recommended for thorough preparation.
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Reviews
This course provides real-world working experience with Azure Machine Learning, from data import to model deployment. Experienced instructors facilitated an easy comprehension of complicated concepts. I highly recommend this course for aspiring data scientists!
Mukund KelaIT Head
Vinsys offers comprehensive coverage of necessary Azure ML concepts. The trainers were knowledgeable, and the cloud labs enhanced my learning so that I could design, train and deploy machine learning models on Azure.
Pritika BanerjeeData Scientist
Vinsys has provided exemplary corporate training. The course content was perfectly designed to address our team's needs, improving our data science handling capabilities in Azure. Vinsys is indeed a great partner for acquiring more sophisticated Azure knowledge.
Rajesh VDelivery Manager
This course includes all important areas, from data exploration to model monitoring. The well-defined training courses, combined with the agility of the support provided, make it perfect for intermediate or advanced learners who want to master Azure ML solutions.
Pranav NigamData Base Enginner
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