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Designing & Implementing a Data Science Solution on Azure (DP-100T01) Certification Course
DP-100 Certification Training
The 4-day instructor-led online DP-100T01: Designing and Implementing a Data Science Solution on Azure training in the UAE teaches professionals to construct and train and launch machine learning models through Azure platforms. The program provides a structured training path to help candidates pa
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DP-100 Certification Training
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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 UAE targets professionals who need to create and deploy machine learning models by using Microsoft Azure. The extensive training program provides all necessary knowledge to pass the Microsoft Certified: Azure Data Scientist Associate certification which enables candidates to create and deploy AI models across cloud environments. The program delivers real-world data science practice through its complete data science process which includes data cleaning steps and model implementation and monitoring tasks.
Through Azure Machine Learning participants will experience direct practice with cloud-based compute resource configuration and large dataset pre-processing and advanced machine learning technique implementation. AutoML and hyperparameter tuning and real-time model inferencing are among the main subjects taught in the program. The training includes Responsible AI principles together with ethical guidelines for AI development and deployment procedures. The training teaches learners about connecting Azure ML to Azure Synapse Analytics and Azure Cognitive Services which helps improve AI-powered decision systems.
The course provides essential knowledge about security measures and governance standards and economical AI deployment methods so learners can build compliant scalable machine learning solutions. The course provides learners with essential technical abilities that enable them to create and deploy AI models through expert training and practical examples and hands-on exercises on Azure platform.
Professionals who finish this course will achieve full readiness for the Azure Data Scientist Associate certification so they can effectively design and deploy enterprise-grade data science solutions in cloud environments.
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Course Objectives
- Gain insights into structuring and executing data science workflows within Microsoft Azure.
- Set up and manage Azure Machine Learning environments, configure cloud-based AI infrastructure, and allocate computing resources effectively.
- Master techniques for preprocessing data, refining features, and selecting models to develop efficient machine learning solutions.
- Utilize Azure Machine Learning Studio and SDK for training, assessing, and enhancing machine learning algorithms.
- Leverage automated machine learning (AutoML) and ML pipelines to streamline AI solution development.
- Deploy machine learning models as web-based services and integrate them with Azure Synapse Analytics and Azure Cognitive Services.
- Apply ethical AI methodologies to promote fairness, transparency, and accountability in AI model deployment.
- Explore security protocols, regulatory compliance, and governance strategies to protect AI models and data assets.
- Use Azure’s built-in monitoring and diagnostic tools to analyze, troubleshoot, and enhance the performance of AI applications.
- Build expertise in Microsoft Azure’s machine learning ecosystem to prepare for the Azure Data Scientist Associate certification.
Audience
- Technical Consultants
- AI/ML Researchers
- Cloud Engineers
- Software Developers
- IT Managers
- Data Scientists
- AI Professionals
- Business Intelligence Analysts
- Enterprise Architects
- Cloud Architects
- Machine Learning Engineers
- IT Professionals
- Data Analysts
- DevOps Engineers
- System Administrators
Eligibility Criteria
Mandatory:
- Setting up and managing cloud-based resources within Microsoft Azure.
- Building, training, and validating machine learning models using widely used frameworks such as Scikit-Learn, PyTorch, and TensorFlow.
- Handling and deploying applications in containerized environments.
Suggested:
- AI-900T00: Fundamentals of Microsoft Azure AI.
- AI+ Executive™ Program.
- AI+ Prompt Engineer™: Beginner 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
Azure Data Scientist Certification:
This certification confirms an individual’s proficiency in data science and their capability to manage machine learning operations within the Azure ecosystem. It is suitable for professionals who develop and implement machine learning models and want to validate their expertise.
Ideal Candidates for This Certification:
Professionals skilled in data science and machine learning methodologies who specialize in creating and maintaining AI-driven solutions on Azure.
Core Responsibilities:
- Configuring and optimizing the infrastructure for machine learning projects.
- Processing and transforming data for analysis.
- Training models for predictive insights.
- Constructing and overseeing ML workflows.
- Running automated tasks for deployment readiness.
- Implementing, scaling, and maintaining ML solutions.
Technologies Included:
- Azure ML Services
- MLflow Framework
Exam Structure and Weighting:
- Setting up ML environments: 20–25%
- Data processing and model development: 35–40%
- Preparing trained models for deployment: 20–25%
- Deploying and maintaining ML models: 10–15%
Exam Information:
- Minimum Score Required: 700/1000
- Time Allotted: 120 minutes
Renewal Guidelines:
- Previously, specialized Microsoft certifications were valid for two years.
- As of June 2021, they now last for one year but can be renewed at no cost via Microsoft Learn.
- The renewal process opens six months before expiration, allowing candidates to take an assessment to extend validity.
- Certifications earned before June 2021 remain valid for two years and qualify for 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 credential in the UAE signify?
This recognition validates proficiency in utilizing data science methodologies and machine learning algorithms to design and oversee ML workflows on Microsoft Azure. It is tailored for professionals focusing on AI-driven and cloud-based machine learning implementations.
Are there any eligibility criteria for this certification?
There are no mandatory requirements, but familiarity with data science principles, Python programming, and machine learning tools is beneficial. Prior exposure to Azure Machine Learning and MLflow is strongly advised.
Which test must be cleared to obtain this certification?
Aspirants must successfully pass Exam DP-100: Designing and Implementing a Data Science Solution on Azure, which evaluates expertise in data handling, model construction, MLOps integration, and cloud-based deployment strategies.
How long is the certification valid?
The certification remains active for one year. Renewal is available at no cost through an online assessment, accessible six months before expiration.
What is the passing threshold for Exam DP-100 in the UAE?
A minimum score of 700 out of 1000 is required. Since the exam follows a scaled grading system, the precise number of correct responses needed may vary.
What subjects are covered in the Azure Data Scientist Associate training?
The curriculum includes:
- Setting up and fine-tuning machine learning environments
- Data refinement and feature engineering
- Model development, assessment, and hyperparameter optimization
- Implementing MLOps methodologies and automation pipelines
- Deploying and overseeing ML models within Azure
Who should consider this training?
This program is ideal for data scientists, AI practitioners, and machine learning professionals responsible for designing and deploying ML solutions on Azure. It is also beneficial for experts in cloud computing, data analytics, and AI domains.
Does the course include practical exercises?
Yes, participants engage in interactive labs and real-world scenarios, gaining hands-on experience in building, training, deploying, and managing ML models within Azure.
What is the duration of the Azure Data Scientist Associate training?
The course typically spans four days, incorporating instructor-led sessions, practical exercises, and exam-focused preparation.
Will this program assist in preparing for Exam DP-100 in the UAE?
Yes, the training is structured around the DP-100 exam syllabus, providing comprehensive insights into key topics. However, supplementary self-study and mock assessments are recommended for thorough readiness.
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Reviews
Comprehensive and Hands-On! This program combines core principles with practical execution, making it perfect for data experts. Skilled instructors break down intricate subjects like model creation, MLOps, and cloud-based deployment. Interactive labs offer real-world practice, while in-depth discussions on Azure Machine Learning and MLflow enhance understanding. Engaging Q&A sessions address uncertainties, increasing confidence in handling machine learning projects on Azure.
Srinivas KunchalaProject Manager
Essential for AI and Data Science Professionals! Covering everything from data preparation to model deployment, this training focuses on practical implementation. Step-by-step guidance, structured tasks, and problem-solving techniques strengthen comprehension. With a strong emphasis on hyperparameter tuning, automated ML, and ethical AI, the course ensures industry relevance, equipping participants with critical skills.
VR RajeshData Scientist
Game-Changing for ML Experts! A well-structured learning path, combined with expert-led insights and real-world case studies, simplifies intricate concepts. Sessions on feature engineering, model evaluation, and MLOps frameworks stand out as particularly beneficial. Completing this course has enhanced my knowledge and prepared me for advanced roles in AI and cloud-based machine learning.
Diksha AggarwalData Engineer
Premier Azure ML Training in the UAE for Every Experience Level! Whether you're just starting out or a seasoned ML expert, this program covers everything from experimentation to large-scale deployment. Hands-on labs and live simulations create an engaging learning experience. A deep dive into Azure Machine Learning, responsible AI, and pipeline automation improves problem-solving skills and optimization strategies for developing scalable AI solutions.
Manoj YadavCloud Security Engineer
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