DP-100 Designing and Implementing a Data Science Solution on Azure

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Multisoft Systems is offering project-driven training on DP-100 Designing and Implementing a Data Science Solution on operating machine learning solutions at a cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Our offered course demands in-depth knowledge of cloud computing, core data concepts, and experience with data solutions. Multisoft insists you pursue this course after you have earned the AZ-900 - Azure Fundamentals and DP-900 - Microsoft Azure Data Fundamentals courses.

DP-100 Designing and Implementing a Data Science Solution on Azure Course Objectives:
  • How to run interactive queries using serverless SQL pools?
  • How to transform data with Azure Data Factory or Azure Synapse Pipelines?
  • How to work with DataFrames advanced methods in Azure Databricks?
  • How to perform end-to-end security with Azure Synapse Analytics?
  • How to ingest and load Data into the Data Warehouse?
  • How to integrate Data from Notebooks with Azure Synapse Pipelines or Azure Data Factory?
  • How to describe big data engineering with Apache Spark in Azure Synapse Analytics?
  • How to transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics?
  • How to perform data Exploration and Transformation in Azure Databricks?
DP-100 Designing and Implementing a Data Science Solution on Azure Online Training
  • 28 Hrs. Instructor-led Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support
Target audience
  • Data architects
  • Data professionals
  • Data Scientist
  • Business intelligence professionals who want to learn data engineering
  • Data analysts who are responsible for building Microsoft Azure
  • Data scientists who work with analytical solutions
DP-100 Designing and Implementing a Data Science Solution on Azure Course Prerequisites
  • You need to have in-depth knowledge of cloud computing to pursue this DP-203: Data Engineering on Microsoft Azure Online Training. Knowing core data concepts and having professional experience with data solutions are necessary for earning this course.
  • AZ-900 - Azure Fundamentals
  • DP-900 - Microsoft Azure Data Fundamentals
DP-100 Designing and Implementing a Data Science Solution on Azure Course Certification
  • Multisoft Systems will provide you with a training completion certificate after completing the DP-203: Data Engineering on Microsoft Azure Training.

Manage Azure resources for machine learning (25-30%)

Create an Azure Machine Learning workspace

  • create an Azure Machine Learning workspace
  • configure workspace settings
  • manage a workspace by using Azure Machine Learning studio

Manage data in an Azure Machine Learning workspace

  • select Azure storage resources
  • register and maintain datastores
  • create and manage datasets

Manage compute for experiments in Azure Machine Learning

  • determine the appropriate compute specifications for a training workload
  • create compute targets for experiments and training
  • configure Attached Compute resources including Azure Databricks
  • monitor compute utilization

Implement security and access control in Azure Machine Learning

  • determine access requirements and map requirements to built-in roles
  • create custom roles
  • manage role membership
  • manage credentials by using Azure Key Vault

Set up an Azure Machine Learning development environment

  • create compute instances
  • share compute instances
  • access Azure Machine Learning workspaces from other development environments

Set up an Azure Databricks workspace

  • create an Azure Databricks workspace
  • create an Azure Databricks cluster
  • create and run notebooks in Azure Databricks
  • link and Azure Databricks workspace to an Azure Machine Learning workspace

Run experiments and train models (20-25%)

Create models by using the Azure Machine Learning designer

  • create a training pipeline by using Azure Machine Learning designer
  • ingest data in a designer pipeline
  • use designer modules to define a pipeline data flow
  • use custom code modules in designer

Run model training scripts

  • create and run an experiment by using the Azure Machine Learning SDK
  • configure run settings for a script
  • consume data from a dataset in an experiment by using the Azure Machine Learning
  • SDK
  • run a training script on Azure Databricks compute
  • run code to train a model in an Azure Databricks notebook

Generate metrics from an experiment run

  • log metrics from an experiment run
  • retrieve and view experiment outputs
  • use logs to troubleshoot experiment run errors
  • use MLflow to track experiments
  • track experiments running in Azure Databricks

Use Automated Machine Learning to create optimal models

  • use the Automated ML interface in Azure Machine Learning studio
  • use Automated ML from the Azure Machine Learning SDK
  • select pre-processing options
  • select the algorithms to be searched
  • define a primary metric
  • get data for an Automated ML run
  • retrieve the best model

Tune hyperparameters with Azure Machine Learning

  • select a sampling method
  • define the search space
  • define the primary metric
  • define early termination options
  • find the model that has optimal hyperparameter values

Deploy and operationalize machine learning solutions (35-40%)

Select compute for model deployment

  • consider security for deployed services
  • evaluate compute options for deployment

Deploy a model as a service

  • configure deployment settings
  • deploy a registered model
  • deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
  • consume a deployed service
  • troubleshoot deployment container issues

Manage models in Azure Machine Learning

  • register a trained model
  • monitor model usage
  • monitor data drift

Create an Azure Machine Learning pipeline for batch inferencing

  • configure a ParallelRunStep
  • configure compute for a batch inferencing pipeline
  • publish a batch inferencing pipeline
  • run a batch inferencing pipeline and obtain outputs
  • obtain outputs from a ParallelRunStep

Publish an Azure Machine Learning designer pipeline as a web service

  • create a target compute resource
  • configure an inference pipeline
  • consume a deployed endpoint

Implement pipelines by using the Azure Machine Learning SDK

  • create a pipeline
  • pass data between steps in a pipeline
  • run a pipeline
  • monitor pipeline runs

Apply ML Ops practices

  • trigger an Azure Machine Learning pipeline from Azure DevOps
  • automate model retraining based on new data additions or data changes
  • refactor notebooks into scripts
  • implement source control for scripts

Implement responsible machine learning (5-10%)

Use model explainers to interpret models

  • select a model interpreter
  • generate feature importance data

Describe fairness considerations for models

  • evaluate model fairness based on prediction disparity
  • mitigate model unfairness

Describe privacy considerations for data

  • describe principles of differential privacy
  • specify acceptable levels of noise in data and the effects on privacy

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