Welcome to this
section where you will learn about compute targets. In Azure Machine Learning, compute targets are physical
or virtual computers on which experiments are run. Azure Machine Learning
supports multiple types of compute for
experimentation and training. This enables you to select the most appropriate type of compute target for
your particular needs. These types include:
Local compute, compute clusters and
attached compute. Let's look at each of these
in a little more detail now. You can specify a local
compute target for most processing tasks in
Azure Machine Learning. This runs the experiment on the same compute target as the code used to
initiate the experiment. This may be your
physical workstation or virtual machine such as an Azure Machine
Learning compute instance on which you
are running a notebook. Local compute is generally a great choice during
development and testing, with low to moderate
volumes of data. For experiment workloads with high scalability requirements, you can use Azure Machine
Learning compute clusters, which are multi-node
clusters of virtual machines that automatically scale
up or down to meet demand. This is a cost-effective
way to run experiments that need to handle
large volumes of data, or use parallel
processing to distribute the workload and reduce
the time it takes to run. If you already use an Azure-based
compute environment for data science such as a virtual machine or an
Azure Databricks cluster, you can attach it to your Azure Machine Learning workspace and use it as a compute target for certain types of workload. It's important to note that in Azure Machine Learning Studio, you can create another type of compute named
inference clusters. This kind of compute represents an Azure Kubernetes
Service cluster, and can only be used to deploy trained models as
inferencing services. We'll explore deployment later, but for now we're
focused on compute for experiments and
model training. The ability to assign experiment runs to specific compute targets helps you implement a flexible
data science ecosystem in the following ways. Code can be developed
and tested on local or low-cost
compute and then moved to more scalable compute
for production workloads. You can run individual
processes on the compute target that
best fits its needs. For example, by using CPU-based compute to train
deep learning models, and switching to
lower-cost CPU only compute to test and
register the trained model. One of the core benefits
of Cloud computing is the ability to manage costs by paying only for what you use. In Azure Machine Learning, you can take advantage
of this principle by defining compute targets that start on-demand and stop automatically when
no longer required, and scale automatically based on workload processing needs.