MCA Microsoft Azure Data Scientist w/ MCA Microsoft Azure Data Engineer COMBO Training & Certification Bootcamp – 6 days

No-Code Machine Learning

This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.

Lessons

• Automated Machine Learning
• Azure Machine Learning Designer

Lab : Use Automated Machine Learning

Lab : Use Azure Machine Learning Designer

After completing this module, you will be able to

• Use automated machine learning to train a machine learning model
• Use Azure Machine Learning designer to train a model

Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

• Introduction to Experiments
• Training and Registering Models

Lab : Run Experiments

Lab : Train Models

After completing this module, you will be able to

• Run code-based experiments in an Azure Machine Learning workspace
• Train and register machine learning models

Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

• Working with Datastores
• Working with Datasets

Lab : Work with Data

After completing this module, you will be able to

• Create and use datastores
• Create and use datasets

Working with Compute

One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

• Working with Environments
• Working with Compute Targets

Lab : Work with Compute

After completing this module, you will be able to

• Create and use environments
• Create and use compute targets

Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Lessons

• Introduction to Pipelines
• Publishing and Running Pipelines

Lab : Create a Pipeline

After completing this module, you will be able to

• Create pipelines to automate machine learning workflows
• Publish and run pipeline services

Deploying and Consuming Models

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

• Real-time Inferencing
• Batch Inferencing
• Continuous Integration and Delivery

Lab : Create a Real-time Inferencing Service

Lab : Create a Batch Inferencing Service

After completing this module, you will be able to

• Publish a model as a real-time inference service
• Publish a model as a batch inference service
• Describe techniques to implement continuous integration and delivery

Training Optimal Models

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

• Hyperparameter Tuning
• Automated Machine Learning

Lab : Tune Hyperparameters

Lab : Use Automated Machine Learning from the SDK

After completing this module, you will be able to

• Optimize hyperparameters for model training
• Use automated machine learning to find the optimal model for your data

Responsible Machine Learning

Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.

Lessons

• Differential Privacy
• Model Interpretability
• Fairness

Lab : Explore Differential provacy

Lab : Interpret Models

Lab : Detect and Mitigate Unfairness

After completing this module, you will be able to

• Apply differential provacy to data analysis
• Use explainers to interpret machine learning models
• Evaluate models for fairness

Monitoring Models

Azure for the Data Engineer

This module explores how the world of data has evolved and how cloud data platform technologies are providing new opportunities for businesses to explore their data in different ways. The students will gain an overview of the various data platform technologies that are available and how a Data Engineer’s role and responsibilities has evolved to work in this new world to an organization’s benefit.

Lessons

• Explain the evolving world of data
• Survey the services in the Azure Data Platform
• Identify the tasks that are performed by a Data Engineer
• Describe the use cases for the cloud in a Case Study

Lab : Azure for the Data Engineer

• Identify the evolving world of data
• Determine the Azure Data Platform Services
• Identify tasks to be performed by a Data Engineer
• Finalize the data engineering deliverables

After completing this module, students will be able to:

• Explain the evolving world of data
• Survey the services in the Azure Data Platform
• Identify the tasks that are performed by a Data Engineer
• Describe the use cases for the cloud in a Case Study

Working with Data Storage

This module teaches the variety of ways to store data in Azure. The students will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for the data want to be stored in the cloud. They will also understand how Data Lake storage can be created to support a wide variety of big data analytics solutions with minimal effort.

Lessons

• Choose a data storage approach in Azure
• Create an Azure Storage Account
• Explain Azure Data Lake storage
• Upload data into Azure Data Lake

Lab : Working with Data Storage

• Choose a data storage approach in Azure
• Create a Storage Account
• Explain Data Lake Storage
• Upload data into Data Lake Store

After completing this module, students will be able to:

• Choose a data storage approach in Azure
• Create an Azure Storage Account
• Explain Azure Data Lake Storage
• Upload data into Azure Data Lake

Enabling Team Based Data Science with Azure Databricks

This module introduces students to Azure Databricks and how a Data Engineer works with it to enable an organization to perform Team Data Science projects. They will learn the fundamentals of Azure Databricks and Apache Spark notebooks; how to provision the service and workspaces; and how to perform data preparation task that can contribute to the data science project.

Lessons

• Explain Azure Databricks
• Work with Azure Databricks
• Read data with Azure Databricks
• Perform transformations with Azure Databricks

Lab : Enabling Team Based Data Science with Azure Databricks

• Explain Azure Databricks
• Work with Azure Databricks
• Read data with Azure Databricks
• Perform transformations with Azure Databricks

After completing this module, students will be able to:

• Explain Azure Databricks
• Work with Azure Databricks
• Read data with Azure Databricks
• Perform transformations with Azure Databricks

Building Globally Distributed Databases with Cosmos DB

In this module, students will learn how to work with NoSQL data using Azure Cosmos DB. They will learn how to provision the service, how they can load and interrogate data in the service using Visual Studio Code extensions, and the Azure Cosmos DB .NET Core SDK. They will also learn how to configure the availability options so that users are able to access the data from anywhere in the world.

Lessons

• Create an Azure Cosmos DB database built to scale
• Insert and query data in your Azure Cosmos DB database
• Build a .NET Core app for Cosmos DB in Visual Studio Code
• Distribute data globally with Azure Cosmos DB

Lab : Building Globally Distributed Databases with Cosmos DB

• Create an Azure Cosmos DB
• Insert and query data in Azure Cosmos DB
• Build a .Net Core App for Azure Cosmos DB using VS Code
• Distribute data globally with Azure Cosmos DB

After completing this module, students will be able to:

• Create an Azure Cosmos DB database built to scale
• Insert and query data in your Azure Cosmos DB database
• Build a .NET Core app for Azure Cosmos DB in Visual Studio Code
• Distribute data globally with Azure Cosmos DB

Working with Relational Data Stores in the Cloud

In this module, students will explore the Azure relational data platform options, including SQL Database and SQL Data Warehouse. The students will be able explain why they would choose one service over another, and how to provision, connect, and manage each of the services.

Lessons

• Use Azure SQL Database
• Describe Azure SQL Data Warehouse
• Creating and Querying an Azure SQL Data Warehouse
• Use PolyBase to Load Data into Azure SQL Data Warehouse

Lab : Working with Relational Data Stores in the Cloud

• Use Azure SQL Database
• Describe Azure SQL Data Warehouse
• Creating and Querying an Azure SQL Data Warehouse
• Use PolyBase to Load Data into Azure SQL Data Warehouse

After completing this module, students will be able to:

• Use Azure SQL Database
• Describe Azure Data Warehouse
• Create and Query an Azure SQL Data Warehouse
• Use PolyBase to Load Data into Azure SQL Data Warehouse

Performing Real-Time Analytics with Stream Analytics

In this module, students will learn the concepts of event processing and streaming data and how this applies to Events Hubs and Azure Stream Analytics. The students will then set up a stream analytics job to stream data and learn how to query the incoming data to perform analysis of the data. Finally, they will learn how to manage and monitor running jobs.

Lessons

• Explain data streams and event processing
• Data Ingestion with Event Hubs
• Processing Data with Stream Analytics Jobs

Lab : Performing Real-Time Analytics with Stream Analytics

• Explain data streams and event processing
• Data Ingestion with Event Hubs
• Processing Data with Stream Analytics Jobs

After completing this module, students will:

• Be able to explain data streams and event processing
• Understand Data Ingestion with Event Hubs
• Understand Processing Data with Stream Analytics Jobs

Orchestrating Data Movement with Azure Data Factory

In this module, students will learn how Azure Data Factory can be used to orchestrate the data movement and transformation from a wide range of data platform technologies. They will be able to explain the capabilities of the technology and be able to set up an end to end data pipeline that ingests and transforms data.

Lessons

• Explain how Azure Data Factory works
• Azure Data Factory Components
• Azure Data Factory and Databricks

Lab : Orchestrating Data Movement with Azure Data Factory

• Explain how Data Factory Works
• Azure Data Factory Components
• Azure Data Factory and Databricks

After completing this module, students will:

• Understand Azure Data Factory and Databricks
• Understand Azure Data Factory Components
• Be able to explain how Azure Data Factory works

Securing Azure Data Platforms

In this module, students will learn how Azure provides a multi-layered security model to protect data. The students will explore how security can range from setting up secure networks and access keys, to defining permission, to monitoring across a range of data stores.

Lessons

• An introduction to security
• Key security components
• Securing Storage Accounts and Data Lake Storage
• Securing Data Stores
• Securing Streaming Data

Lab : Securing Azure Data Platforms

• An introduction to security
• Key security components
• Securing Storage Accounts and Data Lake Storage
• Securing Data Stores
• Securing Streaming Data

After completing this module, students will:

• Have an introduction to security
• Understand key security components
• Understand securing Storage Accounts and Data Lake Storage
• Understand securing Data Stores
• Understand securing Streaming Data

Monitoring and Troubleshooting Data Storage and Processing