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Designing and Implementing a Data Science Solution on Azure
Alert MeDescription
Designing and Implementing a Data Science Solution on Azure (DP-100) Introduction:
Welcome to our in-depth training “Designing and Implementing a Data Science Solution on Azure (DP-100).” This course is meticulously structured for data scientists who are already familiar with Python programming, machine learning frameworks such as Scikit-Learn, PyTorch, TensorFlow, and the foundational concepts of cloud computing.
Our primary focus is to enhance your capabilities in utilizing Microsoft Azure for sophisticated data science solutions. If your objective is to broaden your data science skills or to efficiently implement data science projects using Azure’s powerful tools, then this course is tailor-made for you.
Throughout this course, you will learn how to manage machine learning solutions at a cloud scale through Azure Machine Learning.
We will guide you through each step of the data science process on Azure, starting from data ingestion and preparation, moving through to model training, and culminating with deployment and monitoring. By leveraging your existing knowledge in Python and machine learning, this course provides a hands-on approach to mastering the integration and application of these technologies in Azure’s environment.
Our goal is to equip you with the expertise to efficiently manage and deploy data science solutions, ensuring they are scalable, robust, and secure.
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 |