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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 Qatar provides professionals with the necessary skills to create machine learning models for Microsoft Azure deployment and training. The extensive training provides everything neede
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 course in Qatar targets data scientists and AI professionals who need to construct, deploy and enhance machine learning models through Microsoft Azure. The Microsoft Certified: Azure Data Scientist Associate certification validates participant skills in cloud data science technique application through this training course. The system offers an organized method to handle the complete machine learning cycle that extends from data cleansing to model development and system execution and performance tracking.
The program provides practical Azure Machine Learning training which teaches participants how to set up cloud-based computing resources as well as automate machine learning operations and optimize model accuracy. The training includes detailed instruction on feature engineering and hyperparameter tuning as well as real-time inference methods. The program shows how machine learning models work together with Azure Synapse Analytics and Azure Cognitive Services to boost business intelligence through AI methods.
Security alongside compliance and cost management represent essential components of the training because they enable participants to deploy effective and scalable AI solutions that adhere to best practices. Through its Responsible AI principles, the course teaches professionals to develop ethical machine learning models that remain easy to interpret. Learners acquire technical AI application development skills for Azure through a combination of instructor instruction and practical case work and interactive laboratories.
Participants who successfully finish the program will be ready to achieve the Azure Data Scientist Associate certification which proves their expertise in developing enterprise-level machine learning solutions for Microsoft Azure.
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Course Objectives
- Grasp the entire machine learning workflow on Microsoft Azure, covering data collection, model implementation, and continuous optimization.
- Set up and oversee Azure Machine Learning workspaces, computational infrastructure, and configurations for streamlined AI development.
- Build practical expertise in data cleansing, attribute selection, and exploratory data examination using Azure-based solutions.
- Construct, assess, and refine machine learning models using Azure Machine Learning Studio and its software development kit.
- Apply parameter tuning, automated machine learning, and ML pipelines to improve model precision and optimize processes.
- Deploy AI models as scalable APIs and connect them with Azure Synapse Analytics and other intelligent services.
- Incorporate ethical AI methodologies to promote transparency, fairness, and responsible AI implementation.
- Implement security, regulatory compliance, and data protection measures to safeguard AI solutions within Azure platforms.
- Track model performance, assess operational insights, and resolve challenges using Azure’s AI monitoring capabilities.
- Acquire the expertise required to clear the Microsoft Certified: Azure Data Scientist Associate exam and progress in AI-centric careers.
Audience
- IT Professionals
- Cloud Architects
- Software Developers
- Data Analysts
- Business Intelligence Analysts
- IT Managers
- Data Scientists
- Cloud Engineers
- AI/ML Researchers
- System Administrators
- Machine Learning Engineers
- Technical Consultants
- AI Professionals
- DevOps Engineers
- Enterprise Architects
Prerequisite
Essential:
- Configuring and overseeing cloud infrastructure on Microsoft Azure.
- Developing, refining, and assessing machine learning models with popular libraries like Scikit-Learn, PyTorch, and TensorFlow.
- Managing and deploying solutions within containerized ecosystems.
Recommended:
- AI-900T00: Introduction to Microsoft Azure AI.
- AI+ Executive™ Course.
- AI+ Prompt Engineer™: Entry Level.
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
Certification Overview:
Microsoft Certified Azure Data Scientist Associate
This certification affirms a candidate’s ability to apply data science principles and manage machine learning processes within Azure. It is intended for professionals responsible for designing, deploying, and optimizing AI-driven models.
Who Should Pursue This Certification?
Individuals proficient in machine learning and data science techniques who focus on developing AI solutions in Azure environments.
Key Responsibilities:
- Setting up and fine-tuning cloud-based ML infrastructure.
- Cleaning, transforming, and preparing datasets.
- Training models for predictive analytics.
- Creating and managing end-to-end ML workflows.
- Automating deployment tasks for seamless integration.
- Deploying and maintaining machine learning applications at scale.
Technologies Covered
- Azure Machine Learning Services
- MLflow Framework
Exam Breakdown and Scoring
- Configuring ML environments: 20–25%
- Data preparation and model building: 35–40%
- Preparing models for deployment: 20–25%
- Deploying and managing ML models: 10–15%
Exam Details :
- Passing Score: 700/1000
- Duration: 120 minutes
Certification Renewal:
- Previously, Microsoft certifications had a two-year validity.
- As of June 2021, they are valid for one year but can be renewed for free via Microsoft Learn.
- The renewal process begins six months before expiration, requiring an online assessment.
- Certifications earned before June 2021 remain valid for two years and follow the updated 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 Qatar validate?
This certification verifies expertise in applying data science and machine learning techniques to develop, train, and manage models on Microsoft Azure. It is designed for professionals working with AI-driven analytics and cloud-based machine learning solutions.
Are there any prerequisites for this certification?
No mandatory prerequisites exist, but candidates should have a strong understanding of data science principles, Python programming, and machine learning frameworks. Prior experience with Azure Machine Learning and MLflow is highly beneficial.
Which exam is required to earn this certification?
Candidates must pass Exam DP-100: Designing and Implementing a Data Science Solution on Azure, which evaluates proficiency in data preparation, model training, MLOps integration, and cloud-based model deployment.
How long is the certification valid?
This certification remains valid for one year. Candidates can renew it for free by completing an online assessment, which becomes available six months before the expiration date.
What is the required passing score for the DP-100 exam in Qatar?
A minimum score of 700 out of 1000 is required to pass. Microsoft applies a scaled scoring system, meaning the number of correct responses needed may vary.
What topics are covered in the DP-100T01 training course in Qatar?
This course focuses on essential areas such as:
- Configuring and managing machine learning environments
- Data preparation, feature engineering, and transformation techniques
- Model training, evaluation, and hyperparameter optimization
- MLOps implementation and automation workflows
- Deploying, monitoring, and managing ML models in Azure
Who should take this course?
This training is ideal for data scientists, machine learning engineers, and AI specialists responsible for developing and deploying ML solutions using Azure. It is also valuable for professionals in cloud computing, data analytics, and artificial intelligence.
Does the course include practical exercises?
Yes, participants engage in interactive labs and case studies, gaining hands-on experience in building, training, deploying, and optimizing machine learning models within the Azure ecosystem.
How long does the DP-100T01 course in Qatar take to complete?
The course typically spans four days, featuring instructor-led sessions, hands-on labs, and exam-focused exercises to ensure thorough preparation.
Will this course fully prepared me for the DP-100 exam in Qatar?
Yes, the training is aligned with the DP-100 exam objectives, offering detailed insights into all relevant topics. However, additional self-study and practice tests are recommended for comprehensive exam readiness.
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Reviews
Well-structured and engaging! This course balances theoretical knowledge with hands-on application, making it highly beneficial for data professionals. Instructor-led sessions simplify complex topics like data ingestion, model development, and cloud-based deployment. Practical labs offer real-world experience, while discussions on Azure Machine Learning and MLflow provide deeper insights. Interactive Q&A sessions help reinforce key concepts, ensuring confidence in designing and managing machine learning solutions on Azure.
Qasim Al MirzaData Engineer
A must for data scientists and AI engineers! Covering everything from data pre-processing to deploying ML models, this training focuses on real-world implementation. Step-by-step exercises, structured learning modules, and troubleshooting techniques improve comprehension. The emphasis on hyperparameter tuning, MLOps automation, and responsible AI makes this course an excellent choice for professionals aiming to enhance their expertise.
Nishita samalSenior Project Manager
Outstanding learning experience! A well-organized curriculum, enriched with industry examples and expert insights, simplifies advanced machine learning concepts. Lessons on feature engineering, model performance optimization, and cloud-based execution stand out as particularly valuable. Completing this course has deepened my understanding of Azure ML, preparing me for advanced roles in AI and data science.
Debalina DebData Scientist
Top Azure ML training in Qatar for all levels! Whether you're new to machine learning or an experienced professional, this course provides a comprehensive dive into data science workflows, model training, and deployment strategies. Hands-on labs and interactive simulations make learning engaging. The in-depth focus on Azure Machine Learning, automated ML pipelines, and real-world AI applications enhances problem-solving skills and prepares participants to build scalable AI solutions.
Rohit NingannavarSenior Project Manager
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