Description
The Microsoft Certified Associate – Microsoft Azure Data Scientist Associate 4 day boot camp focuses on actual job task using Azure Machine Learning Service. Students learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.
The Microsoft Certified Azure Data Scientist Associate boot camp is taught using Microsoft Official Courseware:
DP-100: Designing and Implementing a Data Science Solution on Azure
While attending this 4 day camp – students will take one exam (DP-100) to achieve the Microsoft Certified Azure Data Scientist Associate certification. This hands on, instructor led live camp focuses on the real world responsibilities of an Azure Data Scientist.
Skills Gained:
Design a data ingestion strategy for machine learning projects
Design a machine learning model training solution
Design a model deployment solution
Explore Azure Machine Learning workspace resources and assets
Explore devceloper tools for workspace interaction
Make data available in Azure Machine Learning
Work with environments in Azure Machine Learning
Find the best classification model with Automated Machine Learning
Track model training Jupyter notebooks with MLflow
Run a training script as a command job in Azure Machine learning
Track model training with MLflow in jobs
Run piplines in Azure Machine learning
Perform hyperparameter tuning with Azure Machine Learning
Deploy a model to a managed onlin endpoint
Deploy a model to a batch endpoint
Topics Covered in this Official Boot Camp:
Design a data ingestion strategy for machine learning projects
Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution
Design a machine learning model training solution
Identify machine learning tasks
Choose a service to train a machine learning model
Decide between compute options
Design a model deployment solution
Understand how model will be consumed
Decide on real-time or batch deployment
Explore Azure Machine Learning workspace resources and assets
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace
Explore developer tools for workspace interaction
Explore the studio
Explore the Python SDK
Explore the CLI
Make data available in Azure Machine Learning
Understand URIs
Create a datastore
Create a data asset
Work with compute targets in Azure Machine Learning
Create and use a compute instance
Create and use a compute instance
Create and use a compute cluster
Work with environments in Azure Machine Learning
Understand environments
Explore and use curated environments
Create and use custom environments
Find the best classification model with Automated Machine Learning
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
Track model training in Jupyter notebooks with MLflow
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
Run a training script as a command job in Azure Machine Learning
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
Track model training with MLflow in jobs
Track metrics with MLflow
View metrics and evaluate models
Run pipelines in Azure Machine Learning
Create components
Create a pipeline
Run a pipeline job
Perform hyperparameter tuning with Azure Machine Learning
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
Deploy a model to a managed online endpoint
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
Deploy a model to a batch endpoint
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints