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Designing and Implementing a Data Science Solution on Azure (DP-100)
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Designing and Implementing a Data Science Solution on Azure (DP-100) Introduction
Welcome to our comprehensive training course, “Designing and Implementing a Data Science Solution on Azure (DP-100).” This course is designed to empower proficient data scientists who are already familiar with Python and machine learning frameworks to elevate their expertise by harnessing the power of Microsoft Azure for advanced data science solutions.
Throughout this course, our structured curriculum will guide you through every aspect of the data science process on Azure, from data ingestion and preparation to model training, deployment, and monitoring. You will gain practical insights and hands-on experience, enabling you to master each step with confidence.
Our emphasis on scalability, robustness, and security ensures that you will be equipped with the necessary skills to efficiently manage and deploy data science solutions in Azure’s dynamic environment.
By the end of this course, you will have the expertise to deliver impactful results, making the most of Azure’s potent tools for data science. Join us as we embark on this journey to unlock the full potential of data science on Azure.
Designing and Implementing a Data Science Solution on Azure (DP-100) Course Objectives
- Master Data Ingestion and Preparation: Learn how to effectively ingest and prepare data for analysis within the Azure environment, utilizing Azure’s powerful data processing capabilities to handle diverse datasets efficiently.
- Advanced Model Training Techniques: Dive deep into advanced model training techniques, exploring cutting-edge algorithms and methodologies to optimize model performance and accuracy for various data science tasks.
- Deployment and Monitoring: Understand the process of deploying machine learning models in Azure, including best practices for deployment, scalability, and monitoring to ensure the reliability and effectiveness of deployed solutions.
- Scalability and Robustness: Gain insights into designing scalable and robust data science solutions on Azure, leveraging Azure’s infrastructure and services to handle large-scale data processing and analysis while maintaining performance and reliability.
- Security Best Practices: Learn essential security measures and best practices for data science solutions on Azure, ensuring data privacy, compliance, and protection against potential threats and vulnerabilities.
- Practical Application: Apply theoretical concepts through hands-on labs and real-world scenarios, reinforcing learning outcomes and equipping you with practical skills to tackle data science challenges in diverse business environments.
Prerequisites
To get the most from this course, ensure you have these prerequisites:
- Fundamental understanding of cloud computing concepts.
- Proficiency in general data science and ML tools, including:
- Creating Azure resources.
- Python for data exploration and visualization.
- Training and validating ML models using Scikit-Learn, PyTorch, and TensorFlow.
- Familiarity with containerization.
Audience
- Data scientists with Python and ML framework experience.
- Individuals wanting to build and manage ML solutions in Azure.
Designing and Implementing a Data Science Solution on Azure (DP-100) Outline
Getting Started with Azure Machine Learning
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
- Lab: Create an Azure Machine Learning Workspace
No-Code Machine Learning
- Automated Machine Learning
- Azure Machine Learning Designer
- Lab: Use Automated Machine Learning
- Lab: Use Azure Machine Learning Designer
Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
- Lab: Run Experiments
- Lab: Train Models
Working with Data
- Working with Datastores
- Working with Datasets
- Lab: Work with Data
Working with Compute
- Working with Environments
- Working with Compute Targets
- Lab: Work with Compute
Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
- Lab: Create a Pipeline
Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
- Continuous Integration and Delivery
- Lab: Create a Real-time Inferencing Service
- Lab: Create a Batch Inferencing Service
Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
- Lab: Tune Hyperparameters
- Lab: Use Automated Machine Learning from the SDK
Responsible Machine Learning
- Differential Privacy
- Model Interpretability
- Fairness
- Lab: Explore Differential privacy
- Lab: Interpret Models
- Lab: Detect and Mitigate Unfairness
Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
- Lab: Monitor a Model with Application Insights
- Lab: Monitor Data Drift
PreRequisites
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
Specifically:
- Creating cloud resources in Microsoft Azure.
- Using Python to explore and visualize data.
- Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
- Working with containers
Audience
This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.
$2595.00
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4 Days Course |