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■ Foreword
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• ML Studio, an integrated development environment for ML,
lets you set up experiments as simple data flow graphs, with an
easy-to-use drag, drop, and connect paradigm. Data scientists
can avoid programming for a large number of common tasks,
allowing them to focus on experiment design and iteration.
• Many sample experiments are provided to make it easy to get
started.
• A collection of best-of-breed algorithms developed by Microsoft
Research is built in, as is support for custom R code. Over 350
open source R packages can be used securely within Azure ML.
• Data flow graphs can have several parallel paths that
automatically run in parallel, allowing scientists to execute
complex experiments and make side-by-side comparisons
without the usual computational constraints.
• Experiments are readily sharable, so others can pick up on your
work and continue where you left off.
Azure ML also makes it simple to create production deployments at scale in the
cloud. Pretrained ML models can be incorporated into a scoring workflow and, with
a few clicks, a new cloud-hosted REST API can be created. This REST API has been
engineered to respond with low latency. No reimplementation or porting is required,
which is a key benefit over traditional data analytics software. Data from anywhere on
the Internet (laptops, websites, mobile devices, wearables, and connected machines)
can be sent to the newly created API to get back predictions. For example, a data
scientist can create a fraud detection API that takes transaction information as input
and returns a low/medium/high risk indicator as output. Such an API would then be
“live” on the cloud, ready to accept calls from any software that a developer chooses to
call it from. The API backend scales elastically, so that when transaction rates spike, the
Azure ML service can automatically handle the load. There are virtually no limits on the
number of ML APIs that a data scientist can create and deploy–and all this without any
dependency on engineering. For engineering and IT, it becomes simple to integrate a
new ML model using those REST APIs, and testing multiple models side-by-side before
deployment becomes easy, allowing dramatically better agility at low cost. Azure provides
mechanisms to scale and manage APIs in production, including mechanisms to measure
availability, latency, and performance. Building robust, highly available, reliable ML
systems and managing the production deployment is therefore dramatically faster,
cheaper, and easier for the enterprise, with huge business benefits.
We believe Azure ML is a game changer. It makes the incredible potential of ML
accessible both to startups and large enterprises. Startups are now able to use the same
capabilities that were previously available to only the most sophisticated businesses.
Larger enterprises are able to unleash the latent value in their big data to generate
significantly more revenue and efficiencies. Above all, the speed of iteration and
experimentation that is now possible will allow for rapid innovation and pave the way for
intelligence in cloud-connected devices all around us.
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