IL - DP-100: Designing and Implementing a Data Science Solution on Azure
In this course students will gain the necessary knowledge about how to use Azure services to develop, train, and deploy, machine learning solutions. The course starts with an overview of Azure services that support data science. From there, it focuses on using Azure's premier data science service, Azure Machine Learning service, to automate the data science pipeline. This course is focused on Azure and does not teach the student how to do data science. It is assumed students already know that.
Pass the DP-100 Designing and Implementing a Data Science Solution on Azure exam to be awarded the Microsoft Certified: Azure Data Scientist Associate certification.
Students learn how to develop data models that solve business problems using Azure technologies.
The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.
This course is 50% presentation and demonstration and 50% hands-on learning using Microsoft Azure.
- Duration: 3 Days
- Level: 300
Who this course is designed for
- Candidates for this course apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders. Candidates use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives. Candidates use applications that involve natural language processing, speech, computer vision, and predictive analytics.
- Select development environment
- Set up development environment
- Quantify the business problem
- Transform data into usable datasets
- Perform Exploratory Data Analysis (EDA)
- Cleanse and transform data
- Perform feature extraction
- Perform feature selection
- Select an algorithmic approach
- Split datasets
- Identify data imbalances
- Train the model
- Evaluate model performance
- Knowledge of common statistical methods and data analysis best practices
- Working knowledge of relational databases
Module 1: Introduction to Azure Machine Learning
- Getting Started with Azure Machine Learning
- Azure Machine Learning Tools
Module 2: "No-code" Machine Learning with Designer
- Training Models with Designer
- Publishing Models with Designer
Module 3: Running Experiments and Training Models
- Introduction to Experiments
- Training and Registering Models
Module 4: Working with Data
- Working with Datastores
- Working with Datasets
Module 5: Compute Contexts
- Working with Environments
- Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
- Introduction to Pipelines
- Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
- Real-time Inferencing
- Batch Inferencing
Module 8: Training Optimal Models
- Hyperparameter Tuning
- Automated Machine Learning
Module 9: Interpreting Models
- Introduction to Model Interpretability
- Using Model Explainers
Module 10: Monitoring Models
- Monitoring Models with Application Insights
- Monitoring Data Drift
This course can be delivered dedicated to your team either virtually or onsite. A dedicated delivery allows deeper discussion with your team and our instructor on projects and workloads that are specific to your environment.
This course can be customized by adding or removing topics, going deeper on specific topics, or by customizing the delivery schedule to make it easier for your team to attend the training.Contact a Cloud Training Specialist