Professional certification in Azure, Microsoft

MICROSOFT CERTIFIED: AZURE DATA SCIENTIST ASSOCIATE TRAINING DP-100

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Duration & Study Mode:
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Location:
London, Flexible online
Duration: 5 Days / 5 Weeks

You have to appear for the exam named ‘Designing and Implementing a Data Science Solution on Azure’ to become a ‘Microsoft Certified: Azure Data Scientist Associate’. This exam will validate your skill on 

  • Setting up an Azure Machine Learning workspace. 
  • How well you can experiment and train models.
  • How efficiently you can optimize and manage models, and 
  • How effectively you can deploy and consume models.

 

This course has four modules. In the first module, you will be able to set up an Azure Machine Learning Workspace. Here, you will learn to manage data objects and experiment compute contexts. The next module focuses on executing Experiments and Train Models. In this module, you will learn to create models and run training scripts in Azure Machine Learning Workspace. Here, you will also learn to generate metrics from an experiment run. The second last module of this course is all about Optimizing and Managing Models.

Here, you will learn to use automated ML, use hyperdrive to tune hyperparameters, and use model explainers to interpret models. The last module of this training course is all about Deploy and Consume Models. This module will provide you with the required knowledge on how to deploy a model as a service, create a pipeline for batch inferencing, and publish a designer pipeline as a web service.

Prerequisites

  • This course has no specific prerequisites.
  • It is ideal if you have adequate knowledge of Microsoft Azure, Machine Learning, and Data Science.
  • You are expected to have fluency in at least one or more high-level programming languages.

What will you gain after this course

  • You will be recognized as a Microsoft Certified: Azure Data Scientist Associate
  • This course will let you learn more details about Data Science
  • You will be able to earn a wide array of knowledge and skill on AI and ML through this course.
  • You can expand your job opportunities with the help of this certification.

Jobs you can get
with a Azure Data Scientist Associate Certification

  • Data and Applied Scientist
  • Data Scientist – Big Data
  • AI/ML Data Scientist
  • Data Scientist – Human-Computer Interaction (HCI)
  • eCom Data Scientist

Corporate Group Training

  • Customized Training
  • Onsite / Virtual
  • Instructor-led Delivery
  • For small to large groups

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Who is this certification for?

  • Microsoft Azure Developer
  • Microsoft Azure Professionals
  • Data Scientist
  • Data Analyst
  • Machine Learning Specialist


Syllabus

  • Configure workspace settings
  • Manage a workspace by using Azure Machine Learning studio
  • Select Azure storage resources
  • Register and maintain datastores
  • Create and manage datasets
  • 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
  • Determine access requirements and map requirements to built-in roles
  • Create custom roles
  • Manage role membership
  • Manage credentials by using Azure Key Vault
  • Create compute instances
  • Share compute instances
  • Access Azure Machine Learning workspaces from other development environments
  • 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
  • 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
  • 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 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
  • Select a sampling method
  • Define the search space
  • Define the primary metric
  • Define early termination options
  • Find the model that has optimal hyperparameter values
  • Consider security for deployed services
  • Evaluate compute options for deployment
  • 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
  • Register a trained model
  • Monitor model usage
  • Monitor data drift
  • 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
  • Create a target compute resource
  • Configure an inference pipeline
  • Consume a deployed endpoint
  • Create a pipeline
  • Pass data between steps in a pipeline
  • Run a pipeline
  • Monitor pipeline runs
  • 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
  • Select a model interpreter
  • Generate feature importance data
  • Evaluate model fairness based on prediction disparity
  • Mitigate model unfairness
  • Describe principles of differential privacy
  • Specify acceptable levels of noise in data and the effects on privacy

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