MICROSOFT AZURE DATA SCIENTIST – DP-100
The Microsoft Azure Data Scientist certification exam, coded as DP-100, is a Microsoft certification that focuses on assessing a candidate’s knowledge and skills in designing and implementing machine learning models and solutions on the Microsoft Azure cloud platform. Earning the DP-100 certification demonstrates your expertise in designing and implementing machine learning solutions on Azure.
BEST MICROSOFT AZURE DATA SCIENTIST – DP-100 TRAINING IN PUNE | ONLINE
Duration of Training : 40 hrs
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Design and prepare a machine learning solution
Design a machine learning solution
– Determine the appropriate compute specifications for a training workload
– Describe model deployment requirements
– Select which development approach to use to build or train a model
Manage an Azure Machine Learning workspace
– Create an Azure Machine Learning workspace
– Manage a workspace by using developer tools for workspace interaction
– Set up Git integration for source control
Manage data in an Azure Machine Learning workspace
– Select Azure Storage resources
– Register and maintain datastores
– Create and manage data assets
Manage compute for experiments in Azure Machine Learning
– Create compute targets for experiments and training
– Select an environment for a machine learning use case
– Configure attached compute resources, including Apache Spark pools
– Monitor compute utilization
Explore data and train models
Explore data by using data assets and data stores
– Access and wrangle data during interactive development
– Wrangle interactive data with Apache Spark
Create models by using the Azure Machine Learning designer
– Create a training pipeline
– Consume data assets from the designer
– Use custom code components in designer
– Evaluate the model, including responsible AI guidelines
Use automated machine learning to explore optimal models
– Use automated machine learning for tabular data
– Use automated machine learning for computer vision
– Use automated machine learning for natural language processing (NLP)
– Select and understand training options, including preprocessing and algorithms
– Evaluate an automated machine learning run, including responsible AI guidelines
Use notebooks for custom model training
– Develop code by using a compute instance
– Track model training by using MLflow
– Evaluate a model
– Train a model by using Python SDK
– Use the terminal to configure a compute instance
Tune hyperparameters with Azure Machine Learning
– Select a sampling method
– Define the search space
– Define the primary metric
– Define early termination options
Prepare a model for deployment
Run model training scripts
– Configure job run settings for a script
– Configure compute for a job run
– Consume data from a data asset in a job
– Run a script as a job by using Azure Machine Learning
– Use MLflow to log metrics from a job run
