Get DP100 Certification: Azure Data Scientist Associate
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Description of the training
The DP-100: Designing and Implementing an Azure AI Solution is dedicated to the design and implementation of the AIA and machine learning (ML) solutions on Azure. It is ideal for professionals wishing to become experts Azure Data Scientist and prepare for DP100 certification. The program covers tools such as Azure Machine Learning, Azure Databricks and Azure Cognitive Services to help participants develop smart solutions on Azure.
Objectives of training
At the end of this training, participants will be able to:
- Design machine learning solutions on Azure using Azure Machine Learning.
- Create and manage ML pipelines on Azure.
- Train, deploy and evaluate machine learning models using Azure Machine Learning Studio and Azure Databricks.
- Integrate Azure d💬IA services such as Cognitive Services and Bot Services into applications.
- Optimize machine learning models with advanced techniques such as hyperparameter tuning and experiment management.
- Manage development environments and model deployments on Azure.
Who is this training for?
The training is aimed at a wide audience, including:
- Data scientists and data analysts wishing to acquire skills in AI and machine learning on Azure.
- Data professionals with experience in data analysis, wishing to specialize in AI and machine learning on Azure.
- Engineers and developers seeking to integrate AI into corporate applications.
- IT consultants working with Azure, eager to develop their AI skills.
Prerequisites
Before taking this training, it is preferable to have:
- Basic knowledge in mathematics and/or statistics.
- An experience in programming, preferably in Python.
Training programme
Introduction to data science on Azure
- Overview of Azure Machine Learning.
- Understanding Azure roles and tools in a machine learning project.
- Creation of an Azure environment for the development and execution of AI solutions.
- Data collection, cleaning and processing.
- Using Azure Databricks and Azure Machine Learning Studio to work with massive data.
- Creation of data pipelines and dataset management.
- Selection of appropriate algorithms for different data types.
- Model training with scikit-learn, TensorFlow and Keras.
- Application of cross validation techniques and hyperparameter management.
- Deployment of models in production environments via Azure Machine Learning Service.
- Integration of models into applications via RESTful APIs.
- Model management using Azure ML Workbench and performance monitoring.
- Hyperparameter tuning to improve model performance.
- Using Azure Machine Learning Pipelines to optimize workflow.
- Implementation of an experimental management system and monitoring of model results.
- Integration of Azure Cognitive Services (vision, language, sentiment analysis) into IA solutions.
- Using Azure Bot Services to create smart chatbots.
- Implementation of customized AI solutions with Azure Cognitive Services and Custom Vision.
- Evaluation of model performance using standard metrics.
- Model security and access management.
- Compliance of IA solutions with safety standards and regulations.
Training assets
- Preparation for Certification: Training directly aligned with the DP-100 exam.
- Practical training: Practical exercises with Azure tools to acquire concrete skills.
- Azure Expertise: Mastery of Azure tools and services specific to machine learning and artificial intelligence.
- Results-based approach: Clear objectives for successful completion of the certification exam.
- Access to Azure resources: Using Azure Machine Learning, Azure Databricks and Cognitive Services tools for immersive learning.
Pedagogical methods and tools used
- Live Demonstrations: Interaction with data science services on the Azure cloud.
- Practical workshops and real case studies: Application of concepts in various sectors.
- Feedback: Sharing best practices and common mistakes in business.
- Simulations and tools: Using Azure science data resources in interactive workshops.
Evaluation
- MCQ at the end of training.
- Practical case studies.
- Continuous evaluation with personalized feedback.
Normative References
- Azure Well-Architected Framework
- ISO/IEC 19086
- GDPR (General Data Protection Regulation)
- NIST Cloud Computing Standards (SP 500-292)
- ISO 27001 Information security