Hello and welcome
to this course, build and operate machine
learning solutions with Azure Machine Learning. In this course, you
will learn how to use the Azure Machine
Learning Python, SDK, to create and manage
enterprise ready ML solutions. This course is comprised
of the following modules. Use the Azure Machine Learning
SDK to train a model, work with data and compute
in Azure Machine Learning, orchestrate pipelines and deploy real-time machine
learning services with Azure Machine Learning, deployed batch
inference pipelines and tune hyperparameters with
Azure Machine Learning, select models and
protect sensitive data, and monitor machine
learning deployments. Let's start with
the first module. Use the Azure Machine Learning
SDK to train a model. In this course,
you will provision an Azure Machine
Learning workspace, use tools and interfaces to work with Azure
Machine Learning, run code-based experiments in Azure Machine
Learning workspace. Use a script run config to run a model training script as
an Azure Machine Learning, create reusable parameterized
training scripts, and register trained models. In the next module, you
will explore how to train and evaluate classification
and clustering models. Data is the foundation
of machine learning. In this module, you
will learn how to work with data stores
on datasets in Azure Machine Learning
enabling you to build scalable cloud-based
model training solutions. You'll also learn how
to use Cloud compute in Azure Machine Learning to run training experiments at scale. Next, you will learn how to orchestrate pipelines and deploy real-time machine
learning services with Azure Machine Learning. In this module, you'll learn
how to create, publish, and run pipelines to train models in Azure
Machine Learning. You'll also learn how
to register and deploy ML models with the Azure
Machine Learning service. Next, you will
learn how to deploy batch inference pipelines and tune hyperparameters with
Azure Machine Learning. Machine learning models
are often used to generate predictions from
large numbers of observations in a batch process. In this module, you will
accomplish this using Azure Machine Learning to publish a batch
inference pipeline. You will also leverage
cloud-scale experiments to choose optimal hyper parameter
values for model training. Up next, you will learn how to select models and
protect sensitive data. In this module, you
will learn how to use automated machine learning in Azure Machine Learning to find the best model for your data. You will learn how
differential privacy is a leading edge
approach that enables a useful analysis
while protecting individually identifiable
data values. You will also learn
about the factors that influence the
predictions models make. Finally, you will learn how to monitor machine
learning deployments. Machine learning models
can often encapsulate unintentional bias that
results in unfairness. In this module, you will
learn how to use Fairlearn on Azure Machine Learning to detect and mitigate unfairness
in your models. You will learn how to use
telemetry to understand how a machine learning
model is being used once it has been deployed
into production. Finally, you will learn
how to monitor data drift to ensure your model continues
to predict accurately. Additionally,
throughout this course, you will have an
opportunity to explore machine learning models
in Microsoft Azure through interactive
exercises and real-world examples of
data science in action, knowledge checks,
and practice exams. We wish you every success as you start this
learning journey.