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Designing & Implementing a Data Science Solution on Azure (DP-100T01) Certification Training
DP-100 Certification Training
The 4-day instructor-led online DP-100T01: Designing and Implementing a Data Science Solution on Azure training in Saudi Arabia delivers necessary expertise to professionals for designing and implementing machine learning models through Microsoft Azure. The program provides a structured path for
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DP-100 Certification Training
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Course Overview
The DP-100T01: Designing and Implementing a Data Science Solution on Azure program in Saudi Arabia is designed specially to offer hands-on skills to data science experts who want to develop deploy and operate machine learning models through Microsoft Azure. The training program teaches learners to pass the Microsoft Certified: Azure Data Scientist Associate certification which demonstrates their mastery of cloud-based machine learning methodology. The training system uses a systematic method to teach students about data preparation and feature engineering and model training and evaluation and operationalization.
The training provides practical experience with Azure Machine Learning which teaches students how to set up computational resources and create automated pipelines and enhance model performance. The course also covers essential aspects of hyperparameter optimization and model surveillance and real-time inference capabilities to develop reliable AI solutions. The training demonstrates how to combine machine learning models with Azure Synapse Analytics and Azure Cognitive Services to create smooth AI-driven analytics and decision systems.
Throughout the course security and compliance and governance principles receive special emphasis so participants can deploy AI solutions which fulfil industry standards and regulatory requirements. The curriculum teaches learners about designing AI systems through Responsible AI principles that aim to develop transparent and fair and interpretable models. The course provides technical expertise through expert instruction and real-world case studies together with hands-on labs so participants can design and deploy scalable AI solutions on Azure.
Professionals who finish this training will possess the necessary skills to obtain the Azure Data Scientist Associate certification which demonstrates their expertise in developing and managing enterprise-level machine learning models within Microsoft Azure's cloud infrastructure.
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Course Objectives
- Learn the fundamentals of designing and implementing machine learning solutions using Microsoft Azure.
- Set up and manage Azure Machine Learning workspaces, compute resources, and cloud-based AI environments.
- Perform data ingestion, pre-processing, and feature engineering to prepare datasets for machine learning models.
- Build, train, and evaluate machine learning models using Azure Machine Learning Studio and Python SDK.
- Optimize model performance with hyperparameter tuning, AutoML, and machine learning pipelines.
- Deploy models as web services and integrate them with Azure Synapse Analytics and Cognitive Services for real-world applications.
- Implement Responsible AI principles to ensure fairness, transparency, and interpretability in AI solutions.
- Apply security, governance, and compliance best practices to protect machine learning models and sensitive data.
- Monitor, maintain, and retrain deployed models to ensure continuous improvement and accuracy.
- Prepare for the Microsoft Certified: Azure Data Scientist Associate certification by mastering real-world machine learning techniques.
Audience
- Cloud Architects
- Data Analysts
- System Administrators
- AI Professionals
- Data Scientists
- IT Managers
- DevOps Engineers
- AI/ML Researchers
- Technical Consultants
- Software Developers
- Cloud Engineers
- Business Intelligence Analysts
- Machine Learning Engineers
- Enterprise Architects
- IT Professionals
Prerequisite
Required
- Building and managing cloud-based resources within Microsoft Azure
- Developing and accessing machine learning models utilizing frameworks such as Scikit-Learn, PyTorch, and TensorFlow
- Handling and deploying containerized applications
Recommended
- AI-900T00: Fundamentals of Microsoft Azure AI
- AI+ Executive™: Advanced AI leadership training
- AI+ Prompt Engineer™: Level 1 – Foundational prompt engineering techniques
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 verifies expertise in implementing machine learning workflows and data science methodologies within the Azure ecosystem. It is designed for professionals tasked with designing, deploying, and optimizing AI-driven solutions.
Who Should Obtain This Certification?
This certification is ideal for individuals skilled in data science and machine learning, focusing on building and managing AI models in Azure environments.
Core Responsibilities:
- Configuring and managing cloud-based machine learning infrastructure
- Processing, refining, and preparing datasets for analysis
- Training predictive models for various AI applications
- Designing and orchestrating complete machine learning pipelines
- Streamlining deployment for efficient model integration
- Scaling and maintaining ML applications in production
Technologies Covered:
- Azure Machine Learning Services
- MLflow Framework
Exam Content & Weightage:
- Setting up ML environments: 20–25%
- Data processing and model training: 35–40%
- Optimizing models for deployment: 20–25%
- Deploying and managing ML models: 10–15%
Exam Details:
- Minimum Passing Score: 700/1000
- Exam Duration: 120 minutes
Certification Validity & Renewal
Previous Validity Period: Two years
- Updated Policy (as of June 2021): Certifications are now valid for one year
- Renewal Process: Free online assessment available via Microsoft Learn, beginning six months before expiration
- Legacy Certifications: Credentials earned before June 2021 remain valid for two years but must follow the new renewal system
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 credential in Saudi Arabia confirm?
This certification demonstrates proficiency in utilizing data science and machine learning methodologies to create, train, and oversee models within Microsoft Azure. It is intended for professionals handling AI-driven analytics and cloud-based machine learning applications.
Are there any eligibility criteria for this certification?
There are no formal requirements; however, a solid grasp of data science concepts, Python programming, and machine learning frameworks is recommended. Familiarity with Azure Machine Learning and MLflow can be advantageous.
Which examination is necessary to obtain this certification?
Candidates must successfully complete Exam DP-100: Designing and Implementing a Data Science Solution on Azure. This test assesses skills in data pre-processing, model development, MLOps implementation, and deploying ML solutions on the cloud.
What is the certification validity period?
The certification remains active for one year. Candidates can extend its validity at no cost by completing an online renewal assessment, which becomes accessible six months prior to expiration.
What is the minimum passing score for the DP-100 exam?
A score of at least 700 out of 1000 is needed to pass. Microsoft employs a scaled scoring system, so the exact number of correct responses required may fluctuate.
What key areas are addressed in the DP-100T01 training program in Saudi Arabia?
The course covers essential topics, including:
- Configuring and maintaining ML environments
- Data processing, feature engineering, and transformation methods
- Model training, assessment, and hyperparameter tuning
- Implementing MLOps workflows and automation
- Deploying, monitoring, and managing ML models in Azure
Who is the training course designed for?
This program is best suited for data scientists, ML engineers, and AI professionals engaged in developing and implementing machine learning models in Azure. It also benefits individuals in cloud computing, data analysis, and artificial intelligence.
Does the course involve hands-on practice?
Yes, participants will engage in interactive labs and real-world case studies, providing practical experience in building, training, deploying, and optimizing ML models within the Azure platform.
How long does the DP-100T01 training in Saudi Arabia take?
The course typically spans four days, including instructor-led sessions, hands-on labs, and exam-focused activities to ensure a thorough understanding of key concepts.
Does this training ensure exam success?
While the course is aligned with the DP-100 exam objectives and provides in-depth coverage of all key topics, additional self-study and practice tests are recommended for optimal exam readiness.
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Reviews
Vinsys' DP-100T01 course reshaped my data science career. The hands-on experience with Microsoft Azure, Python, and machine learning was immediately applicable to my projects. Engaging online sessions and 24/7 support ensured a thorough preparation for the DP-100 exam. Highly recommended for serious data science enthusiasts.
Uttam KumarData Scientist
Vinsys' DP-100T01 corporate training aligned our data science practices with Azure. The tailored program and customizable skill development proved beneficial. Instructor-led sessions and 24/7 assistance kept our team on track, though more industry-specific case studies would have added value.
Nitin MarkandeyData Engineer
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