DP-100: Designing and Implementing a Data Science Solution on Azure Course
DP-100: Designing and Implementing a Data Science Solution on Azure Certification
DP-100 – Microsoft Azure provides Designing and Implementing a Data Science Solution on Azure certification. (DP-100) – Designing and Implementing a Data Science Solution on Azure certification course gives more advanced level knowledge of Azure data science technologies which help you to build your career in Azure data science. (DP-100) – Designing and Implementing a Data Science Solution on Azure certification offers you to develop your career in Azure services and understand more those services. This certification course validates your expertise in deploy and consumes models, optimise and manage models and run experiments and train models. DP-100 certification training course covers Azure machine learning workloads and data science.
(DP-100) – Designing and Implementing a Data Science Solution on Azure course you will learn more in-depth in creating and planning data science the workload on Azure, running data and predict training models, optimizing and managing models and deploy machine learning models on Azure platform.
Logitrain training centre gives you practical knowledge and theoretical knowledge of Microsoft Azure technology to our students. We provide you to build your technical skills with proper guideline. We help you to develop your career in Microsoft Azure.
Azure data scientist associate certification and knowledge of data science workflow and principal
Candidates can achieve this certification by passing the following exam(s).
- Exam DP-100 – Designing and Implementing a Data Science Solution
The certification exam can be registered and attempted within 3 months of course/module completion at Logitrain training centre on weekdays during normal business hours (excludes public holidays)
Logitrain training centre provides you course material for DP-100 and proper training for this certification.
- Create an Azure Machine Learning workspace
- Manage data objects in an Azure Machine Learning workspace
- Manage experiment compute contexts
- Create models by using Azure Machine Learning designer
- Run training scripts in an Azure Machine Learning workspace
- Generate metrics from an experiment run
- Automate the model training process
- Use Automated ML to create optimal models
- Use Hyperdrive to tune hyperparameters
- Manage models
- Use model explainers to interpret models
- Create a pipeline for batch inferencing
- Publish a pipeline as a web service
- Deploy a model as a service
- Create production compute targets
This course is likely to add to the employment-related skills of the participants. The skills developed are likely to be used in the course of being an employee or working in a business.
- IT professionals who know data science and workloads
- Azure data scientist
- Create an Azure Machine Learning workspace
- Configure workspace settings
- Manage a workspace by using Azure Machine Learning Studio
- Register and maintain data stores
- Create and manage datasets
- Create a compute instance
- Determine appropriate compute specifications for a training workload
- Create compute targets for experiments and training
- 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 the designer
- Create and run an experiment by using the Azure Machine Learning SDK
- Consume data from a data store in an experiment by using the Azure Machine Learning SDK
- Consume data from a dataset in an experiment by using the Azure Machine Learning SDK
- Choose an estimator for a training experiment
- Log metrics from an experiment run
- Retrieve and view experiment outputs
- Use logs to troubleshoot experiment run errors
- Create a pipeline by using the SDK
- Pass data between steps in a pipeline
- Run a pipeline
- Monitor pipeline runs
- Use the Automated ML interface in Azure Machine Learning studio
- Use Automated ML from the Azure Machine Learning SDK
- Select scaling functions and pre-processing options
- Determine 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
- Select a model interpreter
- Generate feature importance data
- Register a trained model
- Monitor model history
- Monitor data drift
- Consider security for deployed services
- Evaluate compute options for the deployment
- Configure deployment settings
- Consume a deployed service
- Troubleshoot deployment container issues
- Publish a batch inferencing pipeline
- Run a batch inferencing pipeline and obtain outputs
- Create a target compute resource
- Configure an Inference pipeline
- Consume a deployed endpoint
Take the certification exam within 3 months of course / module completion
Take the official vendor certification exam at the Logitrain training center
Course material in hardcopy is included. Full-time classroom courses include meals
Mock tests included in full-time courses for 3 months from course completion
Our trainers are highly skilled with expertise and extensive hands-on experience
Relax, we will beat competitor’s advertised price. Our course has no extra costs
The supply of this course/package/program is governed by our terms and conditions. Please read them carefully before enrolling, as enrolment is conditional on acceptance of these terms and conditions.
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